diff --git a/.github/workflows/gitleaks.yml b/.github/workflows/gitleaks.yml
new file mode 100644
index 000000000..4accdeb19
--- /dev/null
+++ b/.github/workflows/gitleaks.yml
@@ -0,0 +1,57 @@
+name: "Gitleaks"
+
+on:
+  pull_request:
+
+env:
+  GIT_GITLEAKS_VERSION: 8.18.4
+  ACTIONS_STEP_DEBUG: '${{ secrets.ACTIONS_STEP_DEBUG }}'
+
+jobs:
+  gitleaks:
+    name: "Gitleaks"
+    runs-on: ubuntu-latest
+    steps:
+      - uses: hmarr/debug-action@v3.0.0
+
+      - name: Configure gitleaks binary cache
+        id: cache
+        uses: actions/cache@v4
+        with:
+          path: /usr/local/bin/gitleaks
+          key: gitleaks-${{ env.GIT_GITLEAKS_VERSION }}
+
+      - name: Install tools
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          wget -O - \
+          "https://github.com/zricethezav/gitleaks/releases/download/v${{ env.GIT_GITLEAKS_VERSION }}/gitleaks_${{ env.GIT_GITLEAKS_VERSION }}_linux_x64.tar.gz" \
+            | sudo tar xzf - -C /usr/local/bin
+
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.pull_request.head.sha || github.event.after }}
+
+      - name: Run gitleaks
+        run: |
+          set -euo pipefail ${ACTIONS_STEP_DEBUG:+-x}
+
+          gitleaks \
+            detect \
+            --source="." \
+            --redact \
+            -v \
+            --exit-code=2 \
+            --report-format=sarif \
+            --report-path=results.sarif \
+            --log-level=debug \
+            --log-opts='${{ github.event.pull_request.base.sha || github.event.before }}..${{ github.event.pull_request.head.sha || github.event.after }}'
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: GitLeaks results
+          path: results.sarif
\ No newline at end of file
diff --git a/README.md b/README.md
index 09083255d..64f06fae5 100644
--- a/README.md
+++ b/README.md
@@ -61,6 +61,8 @@ Check the **Required parameters** column to see if you need to set any additiona
 | [spacy](/label_studio_ml/examples/spacy)                                                   | NER by [SpaCy](https://spacy.io/)                                                                                                                    | ✅              | ❌                | ❌        | None                       | Set      [(see documentation)](https://spacy.io/usage/linguistic-features) |
 | [tesseract](/label_studio_ml/examples/tesseract)                                           | Interactive OCR. [Details](https://github.com/tesseract-ocr/tesseract)                                                                               | ❌              | ✅                | ❌        | None                       | Set (characters)                                                           | 
 | [watsonX](/label_studio_ml/exampels/watsonx)| LLM inference with [WatsonX](https://www.ibm.com/products/watsonx-ai) and integration with [WatsonX.data](watsonx.data)| ✅ | ✅| ❌ | None| Arbitrary|
+| [yolo](/label_studio_ml/examples/yolo)                                                     | Object detection with [YOLO](https://docs.ultralytics.com/tasks/) | ✅ | ❌ | ❌ | None | Arbitrary |
+
 # (Advanced usage) Develop your model
 
 To start developing your own ML backend, follow the instructions below.
diff --git a/label_studio_ml/examples/grounding_dino/README.md b/label_studio_ml/examples/grounding_dino/README.md
index 454c3cc30..5be83ec77 100644
--- a/label_studio_ml/examples/grounding_dino/README.md
+++ b/label_studio_ml/examples/grounding_dino/README.md
@@ -40,7 +40,7 @@ See [here](https://github.com/IDEA-Research/GroundingDINO) for more details abou
 
     Example:
    - `LABEL_STUDIO_HOST=http://123.456.7.8:8080`
-   - `LABEL_STUDIO_ACCESS_TOKEN=c9djf998eii2948ee9hh835nferkj959923`
+   - `LABEL_STUDIO_ACCESS_TOKEN=your-api-key`
 
 3. Run `docker compose up`
 4. Check the IP of your backend using `docker ps`. You will use this URL when connecting the backend to a Label Studio project. Usually this is `http://localhost:9090`.
diff --git a/label_studio_ml/examples/grounding_sam/README.md b/label_studio_ml/examples/grounding_sam/README.md
index f03d98a79..5ed4bbb9c 100644
--- a/label_studio_ml/examples/grounding_sam/README.md
+++ b/label_studio_ml/examples/grounding_sam/README.md
@@ -43,7 +43,7 @@ See [here](https://github.com/IDEA-Research/GroundingDINO) for more details abou
 
     Example:
    - `LABEL_STUDIO_HOST=http://123.456.7.8:8080`
-   - `LABEL_STUDIO_ACCESS_TOKEN=c9djf998eii2948ee9hh835nferkj959923`
+   - `LABEL_STUDIO_ACCESS_TOKEN=your-api-key`
 
 3. Run `docker compose up`
 4. Check the IP of your backend using `docker ps`. You will use this URL when connecting the backend to a Label Studio project. Usually this is `http://localhost:9090`.
diff --git a/label_studio_ml/examples/grounding_sam/docker-compose.yml b/label_studio_ml/examples/grounding_sam/docker-compose.yml
index 4b66e6d9c..a1acf99fd 100644
--- a/label_studio_ml/examples/grounding_sam/docker-compose.yml
+++ b/label_studio_ml/examples/grounding_sam/docker-compose.yml
@@ -3,7 +3,7 @@ version: "3.11"
 services:
   grounding_sam:
     container_name: grounding_sam
-    image: heartexlabs/label-studio-ml-backend:grndsam-master
+    image: heartexlabs/label-studio-ml-backend:grounding_sam-master
     build:
       context: .
       args:
diff --git a/label_studio_ml/examples/mmdetection-3/mmdetection.py b/label_studio_ml/examples/mmdetection-3/mmdetection.py
index 9bf28e82f..622d8bb01 100644
--- a/label_studio_ml/examples/mmdetection-3/mmdetection.py
+++ b/label_studio_ml/examples/mmdetection-3/mmdetection.py
@@ -100,6 +100,7 @@ def build_labels_from_labeling_config(self, schema):
             for ls_label, label_attrs in self.labels_attrs.items():
                 predicted_values = label_attrs.get("predicted_values", "").split(",")
                 for predicted_value in predicted_values:
+                    predicted_value = predicted_value.strip()  # remove spaces at the beginning and at the end
                     if predicted_value:  # it shouldn't be empty (like '')
                         if predicted_value not in mmdet_labels:
                             print(
diff --git a/label_studio_ml/examples/mmdetection-3/requirements-base.txt b/label_studio_ml/examples/mmdetection-3/requirements-base.txt
index d68a65cfd..e7661626e 100644
--- a/label_studio_ml/examples/mmdetection-3/requirements-base.txt
+++ b/label_studio_ml/examples/mmdetection-3/requirements-base.txt
@@ -1,2 +1,2 @@
 gunicorn==22.0.0
-label-studio-ml @ git+https://github.com/HumanSignal/label-studio-ml-backend.git
+label-studio-ml @ git+https://github.com/HumanSignal/label-studio-ml-backend.git@fix/rnd-117
diff --git a/label_studio_ml/examples/mmdetection-3/test_model.py b/label_studio_ml/examples/mmdetection-3/test_model.py
index 329c67d39..33db0b37a 100644
--- a/label_studio_ml/examples/mmdetection-3/test_model.py
+++ b/label_studio_ml/examples/mmdetection-3/test_model.py
@@ -2,7 +2,7 @@
 
 from mmdetection import MMDetection
 
-from pytest import approx
+from label_studio_ml.utils import compare_nested_structures
 
 label_config = """
 <View>
@@ -41,22 +41,6 @@
 ]
 
 
-def compare_nested_structures(a, b, path=""):
-    """Compare two dicts or list with approx() for float values"""
-    if isinstance(a, dict) and isinstance(b, dict):
-        assert a.keys() == b.keys(), f"Keys mismatch at {path}"
-        for key in a.keys():
-            compare_nested_structures(a[key], b[key], path + "." + str(key))
-    elif isinstance(a, list) and isinstance(b, list):
-        assert len(a) == len(b), f"List size mismatch at {path}"
-        for i, (act_item, exp_item) in enumerate(zip(a, b)):
-            compare_nested_structures(act_item, exp_item, path + f"[{i}]")
-    elif isinstance(a, float) and isinstance(b, float):
-        assert a == approx(b), f"Mismatch at {path}"
-    else:
-        assert a == b, f"Mismatch at {path}"
-
-
 def test_mmdetection_model_predict():
     model = MMDetection(label_config=label_config)
     predictions = model.predict([task])
diff --git a/label_studio_ml/examples/yolo/.dockerignore b/label_studio_ml/examples/yolo/.dockerignore
new file mode 100644
index 000000000..fee972bd7
--- /dev/null
+++ b/label_studio_ml/examples/yolo/.dockerignore
@@ -0,0 +1,22 @@
+# Exclude everything
+**
+
+# Include Dockerfile and docker-compose for reference (optional, decide based on your use case)
+!Dockerfile
+!docker-compose.yml
+
+# Include Python application files
+!*.py
+!*.yaml
+!tests/*
+!control_models/*
+!models/*
+
+# Include requirements files
+!requirements*.txt
+
+# Include script
+!*.sh
+
+# Exclude specific requirements if necessary
+# requirements-test.txt (Uncomment if you decide to exclude this)
diff --git a/label_studio_ml/examples/yolo/Dockerfile b/label_studio_ml/examples/yolo/Dockerfile
new file mode 100644
index 000000000..ff417da64
--- /dev/null
+++ b/label_studio_ml/examples/yolo/Dockerfile
@@ -0,0 +1,62 @@
+FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime
+ARG DEBIAN_FRONTEND=noninteractive
+ARG TEST_ENV
+
+WORKDIR /app
+
+RUN conda update conda -y
+
+RUN --mount=type=cache,target="/var/cache/apt",sharing=locked \
+    --mount=type=cache,target="/var/lib/apt/lists",sharing=locked \
+    apt-get -y update \
+    && apt-get install -y git \
+    && apt-get install -y wget \
+    && apt-get install -y g++ freeglut3-dev build-essential libx11-dev \
+    libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev libfreeimage-dev \
+    && apt-get -y install ffmpeg libsm6 libxext6 libffi-dev python3-dev python3-pip gcc
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_CACHE_DIR=/.cache \
+    PORT=9090 \
+    WORKERS=2 \
+    THREADS=4 \
+    CUDA_HOME=/usr/local/cuda
+
+RUN conda install -c "nvidia/label/cuda-12.1.1" cuda -y
+ENV CUDA_HOME=/opt/conda \
+    TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0;7.5;8.0;8.6+PTX;8.9;9.0"
+
+# install base requirements
+COPY requirements-base.txt .
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+    pip install -r requirements-base.txt
+
+# install model requirements
+COPY requirements.txt .
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+    pip3 install -r requirements.txt
+
+# install test requirements if needed
+COPY requirements-test.txt .
+# build only when TEST_ENV="true"
+RUN --mount=type=cache,target=${PIP_CACHE_DIR},sharing=locked \
+    if [ "$TEST_ENV" = "true" ]; then \
+      pip3 install -r requirements-test.txt; \
+    fi
+
+WORKDIR /app
+
+COPY . ./
+
+WORKDIR /app/models
+
+# Download the YOLO models
+RUN yolo predict model=yolov8m.pt source=/app/tests/car.jpg \
+    && yolo predict model=yolov8n.pt source=/app/tests/car.jpg \
+    && yolo predict model=yolov8n-cls.pt source=/app/tests/car.jpg \
+    && yolo predict model=yolov8n-seg.pt source=/app/tests/car.jpg
+
+WORKDIR /app
+
+CMD ["/app/start.sh"]
diff --git a/label_studio_ml/examples/yolo/README.md b/label_studio_ml/examples/yolo/README.md
new file mode 100644
index 000000000..d618fdf6c
--- /dev/null
+++ b/label_studio_ml/examples/yolo/README.md
@@ -0,0 +1,810 @@
+<!--
+---
+title: YOLO ML Backend for Label Studio
+type: guide
+tier: all
+order: 50
+hide_menu: true
+hide_frontmatter_title: true
+meta_title: YOLO ML Backend for Label Studio
+meta_description: Tutorial on how to use an example ML backend for Label Studio with YOLO
+categories:
+    - Computer Vision
+    - Object Detection
+    - Image Segmentation
+    - YOLO
+image: "/tutorials/yolo.png"
+---
+-->
+
+# YOLO ML backend for Label Studio
+
+The YOLO ML backend for Label Studio is designed to integrate advanced object detection, 
+segmentation, classification, and video object tracking capabilities directly into Label Studio. 
+
+This integration allows you to leverage powerful YOLOv8 models for various machine learning tasks, 
+making it easier to annotate large datasets and ensure high-quality predictions. 
+
+<div align="left">
+  <a href="http://www.youtube.com/watch?v=UyaecID1iG8" title="Integrating YOLO V8 with Label Studio">
+    <img src="http://img.youtube.com/vi/UyaecID1iG8/0.jpg" alt="YOLO Integration Video" style="width:50%;"/>
+      <br>
+    Check the video tutorial
+  </a>
+</div>
+<br/>
+
+**Supported Features**
+
+| YOLO Task Name                        | LS Control Tag                       | Prediction Supported | LS Import Supported | LS Export Supported |
+|---------------------------------------|--------------------------------------|----------------------|---------------------|---------------------|
+| Object Detection                      | `<RectangleLabels>`                  | ✅                    | YOLO, COCO          | YOLO, COCO          |
+| Oriented Bounding Boxes (OBB)         | `<RectangleLabels model_obb="true">` | ✅                    | YOLO                | YOLO                |
+| Image Instance Segmentation: Polygons | `<PolygonLabels>`                    | ✅                    | COCO                | YOLO, COCO          |
+| Image Semantic Segmentation: Masks    | `<BrushLabels>`                      | ❌                    | Native              | Native              |
+| Image Classification                  | `<Choices>`                          | ✅                    | Native              | Native              |
+| Pose Detection                        | `<KeyPoints>`                        | ✅                    | Native              | Native              |
+| Video Object Tracking                 | `<VideoRectangle>`                   | ✅                    | Native              | Native              |
+| Video Temporal Classification         | `<TimelineLabels>`                   | Coming soon          | Native              | Native              |
+
+* **LS Control Tag**: Label Studio [control tag](https://labelstud.io/tags/) from the labeling configuration. 
+* **LS Import Supported**: Indicates whether Label Studio supports Import from YOLO format to Label Studio (using the LS converter).
+* **LS Export Supported**: Indicates whether Label Studio supports Export from Label Studio to YOLO format (the **Export** button on the Data Manager and using the LS converter).
+* **Native**: Native means that only native Label Studio JSON format is supported.
+
+
+## Before you begin 
+
+Before you begin, you need to install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [YOLO example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/yolo).
+
+## Quick start
+
+1. Add `LABEL_STUDIO_URL` and `LABEL_STUDIO_API_KEY` to the `docker-compose.yml` file. These variables should point to your Label Studio instance and its API key, respectively. For more information about finding your Label Studio API key, [see our documentation](https://labelstud.io/guide/user_account#Access-token).
+
+2. Run docker compose
+
+    ```bash
+    docker-compose up --build
+    ```
+
+3. Open Label Studio and create a new project with the following labeling config:
+
+    ```xml
+    <View>
+      <Image name="image" value="$image"/>
+      <RectangleLabels name="label" toName="image" model_score_threshold="0.25">
+        <Label value="Car" background="blue" predicted_values="jeep,cab,limousine,truck"/>
+      </RectangleLabels>
+    </View>
+    ```
+
+4. Then from the **Model** page in the project settings, [connect the model](https://labelstud.io/guide/ml#Connect-the-model-to-Label-Studio). The default URL is `http://localhost:9090`. 
+
+5. Add images to Label Studio.
+
+6. Open any task in the Data Manager and see the predictions from the YOLO model.
+
+## Labeling configurations
+
+### Supported object & control tags
+
+**Object tags**
+
+- `<Image>` - [Image to annotate](https://labelstud.io/tags/image)
+- `<Video>` - [Video to annotate](https://labelstud.io/tags/video)
+
+**Control tags**
+
+- `<RectangleLabels>` - [Bounding boxes](https://labelstud.io/tags/rectanglelabels); object detection task
+- `<PolygonLabels>` - [Polygons](https://labelstud.io/tags/polygonlables); segmentation task
+- `<VideoRectangle>` - [Video bounding boxes](https://labelstud.io/tags/videorectangle); object tracking task
+- `<Choices>` - [Classification](https://labelstud.io/tags/choices)
+
+**How to skip the control tag?**
+
+If you don't want to use the ML backend for some control tags, 
+you can force skipping by adding the `model_skip="true"` attribute to the control tag:
+    
+```xml
+<Choices name="choice" toName="image" model_skip="true">
+```
+
+### Mixed object and control tags
+
+You can mix different object and control tags in one project. 
+The YOLO model will detect all known control tags and make predictions for them.
+For example: 
+
+```xml
+<View>
+  <Image name="image1" value="$image"/>
+  <RectangleLabels name="label" toName="image1" model_score_threshold="0.1">
+    <Label value="person" background="red"/>
+    <Label value="car" background="blue"/>
+  </RectangleLabels>
+  
+  <Image name="image2" value="$image"/>
+  <Choices name="choice" toName="image2" model_score_threshold="0.1">
+    <Choice value="airship"/>
+    <Choice value="passenger_car"/>
+  </Choices>
+</View>
+```
+
+In this example, both `RectangleLabels` and `Choices` will be detected and predicted by the YOLO model.
+
+You can also use different YOLO models for the same task to compare them visually:
+
+```xml
+<View>
+  <Image name="image1" value="$image"/>
+  <RectangleLabels name="label1" toName="image1" model_path="yolov8n.pt" model_score_threshold="0.1">
+    <Label value="car" background="blue"/>
+  </RectangleLabels>
+  <RectangleLabels name="label2" toName="image1" model_path="yolov8m.pt" model_score_threshold="0.1">
+    <Label value="car" background="red"/>
+  </RectangleLabels>
+</View>
+```
+
+
+### Label and choice mapping
+
+```mermaid
+graph TD
+    A[Label Studio :: Labeling Config :: Labels or Choices] <--> B[ML Model :: Names]
+```
+
+If you use a common YOLO model, you have to add mapping between your labels and the ML model labels. 
+By default, the YOLO ML backend will use the same (or lowercased) names as you specified in the `value` attribute. 
+
+In this example the label "Jeep" will be mapped to "jeep" in the ML model:
+
+```xml
+<Choice value="Jeep"/> 
+```
+
+For more precise control you can use the `predicted_values` attribute 
+to specify multiple and different labels from the ML model:
+
+```xml
+<Choice value="Car" predicted_values="jeep,cab,limousine"/>
+```
+
+<details>
+<summary>Tip: How to find all YOLO model names?</summary>
+<br/>
+Labels are printed in the ML model logs when you start using the ML backend with the INFO logging level. 
+
+Or you can find some labels in [YOLO_CLASSES.md](YOLO_CLASSES.md)
+</details>
+
+<details>
+<summary>Tip: How to map my labels to YOLO names using an LLM?</summary>
+<br/>
+You can use an LLM model (e.g. ChatGPT) to automatically build mapping between Label Studio labels and ML model labels. 
+Here is an example of a prompt for this. It includes 1000 labels from YOLOv8 classification model (`yolov8n-cls`).
+
+```
+**Task:**
+
+1. **ML Model Labels:**
+   - I have the following labels in my ML model:
+['tench', 'goldfish', 'great_white_shark', 'tiger_shark', 'hammerhead', 'electric_ray', 'stingray', 'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house_finch', 'junco', 'indigo_bunting', 'robin', 'bulbul', 'jay', 'magpie', 'chickadee', 'water_ouzel', 'kite', 'bald_eagle', 'vulture', 'great_grey_owl', 'European_fire_salamander', 'common_newt', 'eft', 'spotted_salamander', 'axolotl', 'bullfrog', 'tree_frog', 'tailed_frog', 'loggerhead', 'leatherback_turtle', 'mud_turtle', 'terrapin', 'box_turtle', 'banded_gecko', 'common_iguana', 'American_chameleon', 'whiptail', 'agama', 'frilled_lizard', 'alligator_lizard', 'Gila_monster', 'green_lizard', 'African_chameleon', 'Komodo_dragon', 'African_crocodile', 'American_alligator', 'triceratops', 'thunder_snake', 'ringneck_snake', 'hognose_snake', 'green_snake', 'king_snake', 'garter_snake', 'water_snake', 'vine_snake', 'night_snake', 'boa_constrictor', 'rock_python', 'Indian_cobra', 'green_mamba', 'sea_snake', 'horned_viper', 'diamondback', 'sidewinder', 'trilobite', 'harvestman', 'scorpion', 'black_and_gold_garden_spider', 'barn_spider', 'garden_spider', 'black_widow', 'tarantula', 'wolf_spider', 'tick', 'centipede', 'black_grouse', 'ptarmigan', 'ruffed_grouse', 'prairie_chicken', 'peacock', 'quail', 'partridge', 'African_grey', 'macaw', 'sulphur-crested_cockatoo', 'lorikeet', 'coucal', 'bee_eater', 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'drake', 'red-breasted_merganser', 'goose', 'black_swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea_anemone', 'brain_coral', 'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea_slug', 'chiton', 'chambered_nautilus', 'Dungeness_crab', 'rock_crab', 'fiddler_crab', 'king_crab', 'American_lobster', 'spiny_lobster', 'crayfish', 'hermit_crab', 'isopod', 'white_stork', 'black_stork', 'spoonbill', 'flamingo', 'little_blue_heron', 'American_egret', 'bittern', 'crane_(bird)', 'limpkin', 'European_gallinule', 'American_coot', 'bustard', 'ruddy_turnstone', 'red-backed_sandpiper', 'redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king_penguin', 'albatross', 'grey_whale', 'killer_whale', 'dugong', 'sea_lion', 'Chihuahua', 'Japanese_spaniel', 'Maltese_dog', 'Pekinese', 'Shih-Tzu', 'Blenheim_spaniel', 'papillon', 'toy_terrier', 'Rhodesian_ridgeback', 'Afghan_hound', 'basset', 'beagle', 'bloodhound', 'bluetick', 'black-and-tan_coonhound', 'Walker_hound', 'English_foxhound', 'redbone', 'borzoi', 'Irish_wolfhound', 'Italian_greyhound', 'whippet', 'Ibizan_hound', 'Norwegian_elkhound', 'otterhound', 'Saluki', 'Scottish_deerhound', 'Weimaraner', 'Staffordshire_bullterrier', 'American_Staffordshire_terrier', 'Bedlington_terrier', 'Border_terrier', 'Kerry_blue_terrier', 'Irish_terrier', 'Norfolk_terrier', 'Norwich_terrier', 'Yorkshire_terrier', 'wire-haired_fox_terrier', 'Lakeland_terrier', 'Sealyham_terrier', 'Airedale', 'cairn', 'Australian_terrier', 'Dandie_Dinmont', 'Boston_bull', 'miniature_schnauzer', 'giant_schnauzer', 'standard_schnauzer', 'Scotch_terrier', 'Tibetan_terrier', 'silky_terrier', 'soft-coated_wheaten_terrier', 'West_Highland_white_terrier', 'Lhasa', 'flat-coated_retriever', 'curly-coated_retriever', 'golden_retriever', 'Labrador_retriever', 'Chesapeake_Bay_retriever', 'German_short-haired_pointer', 'vizsla', 'English_setter', 'Irish_setter', 'Gordon_setter', 'Brittany_spaniel', 'clumber', 'English_springer', 'Welsh_springer_spaniel', 'cocker_spaniel', 'Sussex_spaniel', 'Irish_water_spaniel', 'kuvasz', 'schipperke', 'groenendael', 'malinois', 'briard', 'kelpie', 'komondor', 'Old_English_sheepdog', 'Shetland_sheepdog', 'collie', 'Border_collie', 'Bouvier_des_Flandres', 'Rottweiler', 'German_shepherd', 'Doberman', 'miniature_pinscher', 'Greater_Swiss_Mountain_dog', 'Bernese_mountain_dog', 'Appenzeller', 'EntleBucher', 'boxer', 'bull_mastiff', 'Tibetan_mastiff', 'French_bulldog', 'Great_Dane', 'Saint_Bernard', 'Eskimo_dog', 'malamute', 'Siberian_husky', 'dalmatian', 'affenpinscher', 'basenji', 'pug', 'Leonberg', 'Newfoundland', 'Great_Pyrenees', 'Samoyed', 'Pomeranian', 'chow', 'keeshond', 'Brabancon_griffon', 'Pembroke', 'Cardigan', 'toy_poodle', 'miniature_poodle', 'standard_poodle', 'Mexican_hairless', 'timber_wolf', 'white_wolf', 'red_wolf', 'coyote', 'dingo', 'dhole', 'African_hunting_dog', 'hyena', 'red_fox', 'kit_fox', 'Arctic_fox', 'grey_fox', 'tabby', 'tiger_cat', 'Persian_cat', 'Siamese_cat', 'Egyptian_cat', 'cougar', 'lynx', 'leopard', 'snow_leopard', 'jaguar', 'lion', 'tiger', 'cheetah', 'brown_bear', 'American_black_bear', 'ice_bear', 'sloth_bear', 'mongoose', 'meerkat', 'tiger_beetle', 'ladybug', 'ground_beetle', 'long-horned_beetle', 'leaf_beetle', 'dung_beetle', 'rhinoceros_beetle', 'weevil', 'fly', 'bee', 'ant', 'grasshopper', 'cricket', 'walking_stick', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly', 'damselfly', 'admiral', 'ringlet', 'monarch', 'cabbage_butterfly', 'sulphur_butterfly', 'lycaenid', 'starfish', 'sea_urchin', 'sea_cucumber', 'wood_rabbit', 'hare', 'Angora', 'hamster', 'porcupine', 'fox_squirrel', 'marmot', 'beaver', 'guinea_pig', 'sorrel', 'zebra', 'hog', 'wild_boar', 'warthog', 'hippopotamus', 'ox', 'water_buffalo', 'bison', 'ram', 'bighorn', 'ibex', 'hartebeest', 'impala', 'gazelle', 'Arabian_camel', 'llama', 'weasel', 'mink', 'polecat', 'black-footed_ferret', 'otter', 'skunk', 'badger', 'armadillo', 'three-toed_sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon', 'siamang', 'guenon', 'patas', 'baboon', 'macaque', 'langur', 'colobus', 'proboscis_monkey', 'marmoset', 'capuchin', 'howler_monkey', 'titi', 'spider_monkey', 'squirrel_monkey', 'Madagascar_cat', 'indri', 'Indian_elephant', 'African_elephant', 'lesser_panda', 'giant_panda', 'barracouta', 'eel', 'coho', 'rock_beauty', 'anemone_fish', 'sturgeon', 'gar', 'lionfish', 'puffer', 'abacus', 'abaya', 'academic_gown', 'accordion', 'acoustic_guitar', 'aircraft_carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibian', 'analog_clock', 'apiary', 'apron', 'ashcan', 'assault_rifle', 'backpack', 'bakery', 'balance_beam', 'balloon', 'ballpoint', 'Band_Aid', 'banjo', 'bannister', 'barbell', 'barber_chair', 'barbershop', 'barn', 'barometer', 'barrel', 'barrow', 'baseball', 'basketball', 'bassinet', 'bassoon', 'bathing_cap', 'bath_towel', 'bathtub', 'beach_wagon', 'beacon', 'beaker', 'bearskin', 'beer_bottle', 'beer_glass', 'bell_cote', 'bib', 'bicycle-built-for-two', 'bikini', 'binder', 'binoculars', 'birdhouse', 'boathouse', 'bobsled', 'bolo_tie', 'bonnet', 'bookcase', 'bookshop', 'bottlecap', 'bow', 'bow_tie', 'brass', 'brassiere', 'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof_vest', 'bullet_train', 'butcher_shop', 'cab', 'caldron', 'candle', 'cannon', 'canoe', 'can_opener', 'cardigan', 'car_mirror', 'carousel', "carpenter's_kit", 'carton', 'car_wheel', 'cash_machine', 'cassette', 'cassette_player', 'castle', 'catamaran', 'CD_player', 'cello', 'cellular_telephone', 'chain', 'chainlink_fence', 'chain_mail', 'chain_saw', 'chest', 'chiffonier', 'chime', 'china_cabinet', 'Christmas_stocking', 'church', 'cinema', 'cleaver', 'cliff_dwelling', 'cloak', 'clog', 'cocktail_shaker', 'coffee_mug', 'coffeepot', 'coil', 'combination_lock', 'computer_keyboard', 'confectionery', 'container_ship', 'convertible', 'corkscrew', 'cornet', 'cowboy_boot', 'cowboy_hat', 'cradle', 'crane_(machine)', 'crash_helmet', 'crate', 'crib', 'Crock_Pot', 'croquet_ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop_computer', 'dial_telephone', 'diaper', 'digital_clock', 'digital_watch', 'dining_table', 'dishrag', 'dishwasher', 'disk_brake', 'dock', 'dogsled', 'dome', 'doormat', 'drilling_platform', 'drum', 'drumstick', 'dumbbell', 'Dutch_oven', 'electric_fan', 'electric_guitar', 'electric_locomotive', 'entertainment_center', 'envelope', 'espresso_maker', 'face_powder', 'feather_boa', 'file', 'fireboat', 'fire_engine', 'fire_screen', 'flagpole', 'flute', 'folding_chair', 'football_helmet', 'forklift', 'fountain', 'fountain_pen', 'four-poster', 'freight_car', 'French_horn', 'frying_pan', 'fur_coat', 'garbage_truck', 'gasmask', 'gas_pump', 'goblet', 'go-kart', 'golf_ball', 'golfcart', 'gondola', 'gong', 'gown', 'grand_piano', 'greenhouse', 'grille', 'grocery_store', 'guillotine', 'hair_slide', 'hair_spray', 'half_track', 'hammer', 'hamper', 'hand_blower', 'hand-held_computer', 'handkerchief', 'hard_disc', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home_theater', 'honeycomb', 'hook', 'hoopskirt', 'horizontal_bar', 'horse_cart', 'hourglass', 'iPod', 'iron', "jack-o'-lantern", 'jean', 'jeep', 'jersey', 'jigsaw_puzzle', 'jinrikisha', 'joystick', 'kimono', 'knee_pad', 'knot', 'lab_coat', 'ladle', 'lampshade', 'laptop', 'lawn_mower', 'lens_cap', 'letter_opener', 'library', 'lifeboat', 'lighter', 'limousine', 'liner', 'lipstick', 'Loafer', 'lotion', 'loudspeaker', 'loupe', 'lumbermill', 'magnetic_compass', 'mailbag', 'mailbox', 'maillot_(tights)', 'maillot_(tank_suit)', 'manhole_cover', 'maraca', 'marimba', 'mask', 'matchstick', 'maypole', 'maze', 'measuring_cup', 'medicine_chest', 'megalith', 'microphone', 'microwave', 'military_uniform', 'milk_can', 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing_bowl', 'mobile_home', 'Model_T', 'modem', 'monastery', 'monitor', 'moped', 'mortar', 'mortarboard', 'mosque', 'mosquito_net', 'motor_scooter', 'mountain_bike', 'mountain_tent', 'mouse', 'mousetrap', 'moving_van', 'muzzle', 'nail', 'neck_brace', 'necklace', 'nipple', 'notebook', 'obelisk', 'oboe', 'ocarina', 'odometer', 'oil_filter', 'organ', 'oscilloscope', 'overskirt', 'oxcart', 'oxygen_mask', 'packet', 'paddle', 'paddlewheel', 'padlock', 'paintbrush', 'pajama', 'palace', 'panpipe', 'paper_towel', 'parachute', 'parallel_bars', 'park_bench', 'parking_meter', 'passenger_car', 'patio', 'pay-phone', 'pedestal', 'pencil_box', 'pencil_sharpener', 'perfume', 'Petri_dish', 'photocopier', 'pick', 'pickelhaube', 'picket_fence', 'pickup', 'pier', 'piggy_bank', 'pill_bottle', 'pillow', 'ping-pong_ball', 'pinwheel', 'pirate', 'pitcher', 'plane', 'planetarium', 'plastic_bag', 'plate_rack', 'plow', 'plunger', 'Polaroid_camera', 'pole', 'police_van', 'poncho', 'pool_table', 'pop_bottle', 'pot', "potter's_wheel", 'power_drill', 'prayer_rug', 'printer', 'prison', 'projectile', 'projector', 'puck', 'punching_bag', 'purse', 'quill', 'quilt', 'racer', 'racket', 'radiator', 'radio', 'radio_telescope', 'rain_barrel', 'recreational_vehicle', 'reel', 'reflex_camera', 'refrigerator', 'remote_control', 'restaurant', 'revolver', 'rifle', 'rocking_chair', 'rotisserie', 'rubber_eraser', 'rugby_ball', 'rule', 'running_shoe', 'safe', 'safety_pin', 'saltshaker', 'sandal', 'sarong', 'sax', 'scabbard', 'scale', 'school_bus', 'schooner', 'scoreboard', 'screen', 'screw', 'screwdriver', 'seat_belt', 'sewing_machine', 'shield', 'shoe_shop', 'shoji', 'shopping_basket', 'shopping_cart', 'shovel', 'shower_cap', 'shower_curtain', 'ski', 'ski_mask', 'sleeping_bag', 'slide_rule', 'sliding_door', 'slot', 'snorkel', 'snowmobile', 'snowplow', 'soap_dispenser', 'soccer_ball', 'sock', 'solar_dish', 'sombrero', 'soup_bowl', 'space_bar', 'space_heater', 'space_shuttle', 'spatula', 'speedboat', 'spider_web', 'spindle', 'sports_car', 'spotlight', 'stage', 'steam_locomotive', 'steel_arch_bridge', 'steel_drum', 'stethoscope', 'stole', 'stone_wall', 'stopwatch', 'stove', 'strainer', 'streetcar', 'stretcher', 'studio_couch', 'stupa', 'submarine', 'suit', 'sundial', 'sunglass', 'sunglasses', 'sunscreen', 'suspension_bridge', 'swab', 'sweatshirt', 'swimming_trunks', 'swing', 'switch', 'syringe', 'table_lamp', 'tank', 'tape_player', 'teapot', 'teddy', 'television', 'tennis_ball', 'thatch', 'theater_curtain', 'thimble', 'thresher', 'throne', 'tile_roof', 'toaster', 'tobacco_shop', 'toilet_seat', 'torch', 'totem_pole', 'tow_truck', 'toyshop', 'tractor', 'trailer_truck', 'tray', 'trench_coat', 'tricycle', 'trimaran', 'tripod', 'triumphal_arch', 'trolleybus', 'trombone', 'tub', 'turnstile', 'typewriter_keyboard', 'umbrella', 'unicycle', 'upright', 'vacuum', 'vase', 'vault', 'velvet', 'vending_machine', 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle_iron', 'wall_clock', 'wallet', 'wardrobe', 'warplane', 'washbasin', 'washer', 'water_bottle', 'water_jug', 'water_tower', 'whiskey_jug', 'whistle', 'wig', 'window_screen', 'window_shade', 'Windsor_tie', 'wine_bottle', 'wing', 'wok', 'wooden_spoon', 'wool', 'worm_fence', 'wreck', 'yawl', 'yurt', 'web_site', 'comic_book', 'crossword_puzzle', 'street_sign', 'traffic_light', 'book_jacket', 'menu', 'plate', 'guacamole', 'consomme', 'hot_pot', 'trifle', 'ice_cream', 'ice_lolly', 'French_loaf', 'bagel', 'pretzel', 'cheeseburger', 'hotdog', 'mashed_potato', 'head_cabbage', 'broccoli', 'cauliflower', 'zucchini', 'spaghetti_squash', 'acorn_squash', 'butternut_squash', 'cucumber', 'artichoke', 'bell_pepper', 'cardoon', 'mushroom', 'Granny_Smith', 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard_apple', 'pomegranate', 'hay', 'carbonara', 'chocolate_sauce', 'dough', 'meat_loaf', 'pizza', 'potpie', 'burrito', 'red_wine', 'espresso', 'cup', 'eggnog', 'alp', 'bubble', 'cliff', 'coral_reef', 'geyser', 'lakeside', 'promontory', 'sandbar', 'seashore', 'valley', 'volcano', 'ballplayer', 'groom', 'scuba_diver', 'rapeseed', 'daisy', "yellow_lady's_slipper", 'corn', 'acorn', 'hip', 'buckeye', 'coral_fungus', 'agaric', 'gyromitra', 'stinkhorn', 'earthstar', 'hen-of-the-woods', 'bolete', 'ear', 'toilet_tissue']
+
+
+2. **Labeling Config:**
+   - I have this labeling config from Label Studio:
+   <View>
+     <Image name="image" value="$image"/>
+     <Choices name="choice" toName="image">
+       <Choice value="Airplane"/>
+       <Choice value="Car"/>
+     </Choices>
+   </View>
+
+3. **Mapping Instructions:**
+   - Map the labels from the Label Studio config to the closest matching ML model labels as follows:
+     1. Use the `value` attribute from each `<Choice>` tag to identify the label.
+     2. Find all similar and relevant labels from the ML model corresponding to each `<Choice>` label.
+     3. Add a `predicted_values="<relevant_label1_from_ml_model>,<relevant_label2_from_ml_model>"` attribute inside each `<Choice>` tag using only labels from the ML model.
+
+4. **Output:**
+   - Provide the final labeling config with the `predicted_values` attribute added, using all relevant labels from the ML model, without any explanations.
+```
+
+</details>
+
+## YOLOv5 and other YOLO models
+
+YOLOv8 models have been successfully tested with this ML backend.
+
+Attempts to run YOLOv5 were unsuccessful without modifications. 
+It may be possible to run it by applying some changes, such as installing additional dependencies. 
+The same applies to other YOLO models.
+
+## Custom YOLO models
+
+You can load your own YOLO labels using the following steps:
+
+1. Mount your model as `/app/models/<your-model>.pt` inside of your docker.
+2. Set `ALLOW_CUSTOM_MODEL_PATH=true` (it is true by default) in your Docker environment parameters ([`docker-compose.yml`](docker-compose.yml)).
+3. Add `model_path="<your-model>.pt"` to the control tag in the labeling configuration, e.g.:
+
+```xml
+<RectangleLabels model_path="my_model.pt">
+```
+
+## Training
+
+The current Label Studio ML backend doesn't support training YOLO models. You have to do it manually on your side.
+Or you can contribute to this repository and add training support for this ML backend.
+
+<br>
+
+-------------------
+
+<br>
+
+
+## Classification using `<Choices>`
+
+YOLO provides a classification model and Label Studio supports this with the `Choices` control tag.
+
+More info: https://docs.ultralytics.com/tasks/classify/
+
+https://github.com/user-attachments/assets/30c5ce43-2c89-4ddf-a77d-9d1d75ac3419
+
+
+### Labeling config
+
+```xml
+<View>
+  <Image name="image" value="$image"/>
+  <Choices name="choice" toName="image" model_score_threshold="0.25">
+    <Choice value="Airplane" predicted_values="aircraft_carrier,airliner,airship,warplane"/>
+    <Choice value="Car" predicted_values="limousine,minivan,jeep,sports_car,passenger_car,police_van"/>
+  </Choices>
+</View>
+```
+
+### Parameters
+
+| Parameter               | Type   | Default | Description                                                                                                                                                                                                                                                                                                                                                          |
+|-------------------------|--------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `model_score_threshold` | float  | 0.5     | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.                                                                                                                                                                               |
+| `model_path`            | string | None    | Path to the custom YOLO model. See more in the section "Custom YOLO Models."                                                                                                                                                                                                                                                                                         |
+| `choice`                | string | single  | Possible values: `single`, `single-radio`, `multiple`. If you use `choice="single"` (default) you can select only one label. The ML backend will return the label with the highest confidence using argmax strategy. If you use `choice="multiple"` you can select multiple labels. The ML backend will return all labels with confidence above the `model_score_threshold`. |
+
+
+For example:
+```xml
+<Choices name="choice" toName="image" model_score_threshold="0.25" model_path="my_model.pt">
+```
+
+### Default model
+
+`yolov8n-cls.pt` is the default classification model.
+
+<br>
+
+-------------------
+
+<br>
+
+## Object detection using `RectangleLabels`
+
+YOLO models provide bounding box detection, also known as "object detection." 
+Label Studio supports this with the `RectangleLabels` control tag.
+
+YOLO OBB models are also supported.
+
+More info: https://docs.ultralytics.com/tasks/detect/
+
+https://github.com/user-attachments/assets/413b4650-422d-43dc-809d-51c08f0ad434
+
+
+### Labeling config
+
+```xml
+<View>
+  <Image name="image" value="$image"/>
+  <RectangleLabels name="label" toName="image" model_score_threshold="0.25" opacity="0.1">
+    <Label value="Person" background="red"/>
+    <Label value="Car" background="blue"/>
+  </RectangleLabels>
+</View>
+```
+
+### Parameters
+
+| Parameter               | Type   | Default | Description                                                                                                                                                                            |
+|-------------------------|--------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `model_score_threshold` | float  | 0.5     | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. |
+| `model_path`            | string | None    | Path to the custom YOLO model. See more in the section "Custom YOLO Models."                                                                                                               |
+| `model_obb`             | bool   | False   | Enables Oriented Bounding Boxes (OBB) mode. Typically it uses `*-obb.pt` yolo models.                                                                                                  |
+
+For example:
+```xml
+<RectangleLabels name="label" toName="image" model_score_threshold="0.25" model_path="my_model.pt">
+```
+
+### Default model
+
+`yolov8m.pt` is the default object detection model.
+`yolov8n-obb.pt` is the default OBB object detection model.
+
+### Oriented Bounding Boxes (YOLO OBB)
+
+Oriented (rotated) bounding boxes will be generated automatically if you use an OBB model. 
+Specify `model_obb="true"` in the `RectangleLabels` tag to enable this mode:
+
+```xml
+<RectangleLabels name="label" toName="image" model_score_threshold="0.25" model_obb="true">
+```
+
+More info: https://docs.ultralytics.com/tasks/obb/
+
+<br>
+
+-------------------
+
+<br>
+
+## Segmentation using `PolygonLabels`
+
+YOLO models provide segmentation detection, also known as "instance segmentation." 
+Label Studio supports this with the `PolygonLabels` control tag.
+
+More info: https://docs.ultralytics.com/tasks/segment/
+
+![Yolo Polygons](./YoloPolygons.gif)
+
+https://github.com/user-attachments/assets/9b2447d3-392d-42be-bc7f-ef2b6c81d54c
+
+
+### Labeling config
+
+```xml
+<View>
+  <Image name="image" value="$image"/>
+  <PolygonLabels name="label" toName="image" model_score_threshold="0.25" opacity="0.1">
+    <Label value="Car" background="blue"/>
+    <Label value="Person" background="red"/>
+  </PolygonLabels>
+</View>
+```
+
+### Parameters
+
+| Parameter               | Type   | Default | Description                                                                                                                                                                            |
+|-------------------------|--------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `model_score_threshold` | float  | 0.5     | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. |
+| `model_path`            | string | None    | Path to the custom YOLO model. See more in the section "Custom YOLO Models."                                                                                                               |
+
+For example:
+```xml
+<PolygonLabels name="label" toName="image" model_score_threshold="0.25" model_path="my_model.pt">
+```
+
+### Default model
+
+`yolov8n-seg.pt` is the default segmentation model.
+
+<br>
+
+-------------------
+
+<br>
+
+## Keypoint detection using `KeyPointLabels`
+
+YOLO models provide keypoint detection, also known as "pose estimation." 
+Label Studio supports this with the `KeyPointLabels` control tag.
+
+More info: [Ultralytics YOLO Keypoint Documentation](https://docs.ultralytics.com/tasks/pose/)
+
+![image](https://github.com/user-attachments/assets/72e32125-ba72-47b2-bc9d-a3269d199996)
+
+
+### Labeling config
+
+```xml
+<View>
+  <RectangleLabels name="keypoints_bbox" toName="image" model_skip="true">
+    <Label value="person"/>
+  </RectangleLabels>
+  
+  <KeyPointLabels name="keypoints" toName="image"
+    model_score_threshold="0.75" model_point_threshold="0.5" 
+    model_add_bboxes="true" model_point_size="1"
+    model_path="yolov8n-pose.pt"
+  >
+    <Label value="nose" predicted_values="person" model_index="1" background="red" />
+
+    <Label value="left_eye" predicted_values="person" model_index="2" background="yellow" />
+    <Label value="right_eye" predicted_values="person" model_index="3" background="yellow" />
+
+    <Label value="left_ear" predicted_values="person" model_index="4" background="purple" />
+    <Label value="right_ear" predicted_values="person" model_index="5" background="purple" />
+    
+    <View>
+      <Label value="left_shoulder" predicted_values="person" model_index="6" background="green" />
+      <Label value="left_elbow" predicted_values="person" model_index="8" background="green" />
+      <Label value="left_wrist" predicted_values="person" model_index="10" background="green" />
+
+      <Label value="right_shoulder" predicted_values="person" model_index="7" background="blue" />
+      <Label value="right_elbow" predicted_values="person" model_index="9" background="blue" />
+      <Label value="right_wrist" predicted_values="person" model_index="11" background="blue" />
+    </View>
+    
+    <View>
+      <Label value="left_hip" predicted_values="person" model_index="12" background="brown" />
+      <Label value="left_knee" predicted_values="person" model_index="14" background="brown" />
+      <Label value="left_ankle" predicted_values="person" model_index="16" background="brown" />
+
+      <Label value="right_hip" predicted_values="person" model_index="13" background="orange" />
+      <Label value="right_knee" predicted_values="person" model_index="15" background="orange" />
+      <Label value="right_ankle" predicted_values="person" model_index="17" background="orange" />
+    </View>
+  </KeyPointLabels>
+  
+  <Image name="image" value="$image" />
+</View>
+```
+
+### Parameters
+
+| Parameter               | Type   | Default | Description                                                                                                                                                                    |
+|-------------------------|--------|---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `model_path`            | string | None    | Path to the custom YOLO model. See more in the section "Custom YOLO Models."                                                                                                       |
+| `model_score_threshold` | float  | 0.5     | Sets the minimum confidence threshold for bounding box detections. Keypoints that are related to the detected bbox with a confidence below this threshold will be disregarded. |
+| `model_point_threshold` | float  | 0.0     | Minimum confidence threshold for keypoints. Keypoints with confidence below this value will be ignored.                                                                        |
+| `model_add_bboxes`      | bool   | True    | Adds bounding boxes for detected keypoints. All keypoints will be grouped by parent bounding boxes on the region panel. See details in the tip below.                          |
+| `model_point_size`      | float  | 1       | Size of the keypoints in pixels. Just a visual parameter.                                                                                                                      |
+| `model_index`           | int    | None    | Index of the keypoint in the YOLO model output. It's used in `Label` tags only to build mapping between a Label and an output point.                                           |
+
+For example:
+
+```xml
+<KeyPointLabels name="keypoints" toName="image"
+                model_path="yolov8n-pose.pt"
+                model_score_threshold="0.25" model_point_threshold="0.5" 
+                model_add_bboxes="true" model_point_size="2">
+```
+
+### Default model
+
+`yolov8n-pose.pt` is the default keypoint detection model.
+
+### Grouping keypoints with bounding boxes
+
+When using keypoint detection, the ML backend groups keypoints by the bounding box (bbox) associated with each detected person or object. 
+You will see the grouping under the **Regions** panel on the right side of Label Studio. Note that you can drag and drop region items as necessary.
+
+The bounding boxes are added to the prediction results by default. You can enable or disable this behavior by setting `model_add_bboxes`:
+
+```xml
+<KeyPointLabels name="keypoints" toName="image" model_add_bboxes="false">
+```
+
+<details><summary>Tip: How to only keep keypoints and discard bounding boxes?</summary>
+
+To enable both keypoint detection and bounding box detection in the same task, you have to 
+1. set `model_add_bboxes="false"` in the `KeyPointLabels` tag,
+2. remove `RectangleLabels` tag.
+
+You can use this labeling configuration to get rid of bounding boxes and keep only keypoints:
+
+```xml
+<View>
+  <KeyPointLabels name="keypoints" toName="image"
+    model_score_threshold="0.75" model_point_threshold="0.5" 
+    model_path="yolov8n-pose.pt" model_point_size="1"
+    model_add_bboxes="false"              
+  >
+    <Label value="nose" predicted_values="person" model_index="1" background="red" />
+
+    <Label value="left_eye" predicted_values="person" model_index="2" background="yellow" />
+    <Label value="right_eye" predicted_values="person" model_index="3" background="yellow" />
+
+    <Label value="left_ear" predicted_values="person" model_index="4" background="purple" />
+    <Label value="right_ear" predicted_values="person" model_index="5" background="purple" />
+    
+    <View>
+      <Label value="left_shoulder" predicted_values="person" model_index="6" background="green" />
+      <Label value="left_elbow" predicted_values="person" model_index="8" background="green" />
+      <Label value="left_wrist" predicted_values="person" model_index="10" background="green" />
+
+      <Label value="right_shoulder" predicted_values="person" model_index="7" background="blue" />
+      <Label value="right_elbow" predicted_values="person" model_index="9" background="blue" />
+      <Label value="right_wrist" predicted_values="person" model_index="11" background="blue" />
+    </View>
+    
+    <View>
+      <Label value="left_hip" predicted_values="person" model_index="12" background="brown" />
+      <Label value="left_knee" predicted_values="person" model_index="14" background="brown" />
+      <Label value="left_ankle" predicted_values="person" model_index="16" background="brown" />
+
+      <Label value="right_hip" predicted_values="person" model_index="13" background="orange" />
+      <Label value="right_knee" predicted_values="person" model_index="15" background="orange" />
+      <Label value="right_ankle" predicted_values="person" model_index="17" background="orange" />
+    </View>
+  </KeyPointLabels>
+  <Image name="image" value="$image" />
+</View>
+```
+
+</details>
+
+### Point mapping
+
+For precise control, you can map keypoints to specific labels in your Label Studio configuration. 
+Each keypoint can be associated with a specific part of a person or object, 
+and you can define this mapping using the `model_index` and `predicted_values` attributes.
+
+```xml
+<Label value="left_eye" predicted_values="person" model_index="2" />
+<Label value="right_eye" predicted_values="person" model_index="3" />
+```
+
+This configuration ensures that the keypoints detected by the YOLO model are correctly labeled in the Label Studio interface.
+For pose detection models, the `model_index` attribute is used to map keypoints to specific parts of the body according to the YOLO model output:
+
+```
+0: Nose 1: Left Eye 2: Right Eye 3: Left Ear 4: Right Ear 
+5: Left Shoulder 6: Right Shoulder 7: Left Elbow 8: Right Elbow 9: Left Wrist 10: Right Wrist 
+11: Left Hip 12: Right Hip 13: Left Knee 14: Right Knee 15: Left Ankle 16: Right Ankle
+```
+
+### Recommendations
+
+- **Bounding Box Visualization**: Use the `model_add_bboxes` parameter to visualize the bounding box containing the keypoints. This is especially useful when dealing with multiple detected persons or objects.
+- **Threshold Adjustment**: Adjust the `model_score_threshold` and `model_point_threshold` parameters based on your dataset and the confidence level required for accurate keypoint detection.
+
+<br>
+
+-------------------
+
+<br>
+
+## Video object tracking using `VideoRectangle` 
+
+YOLO models provide object tracking, also known as "multi-object tracking."
+Label Studio supports this with the `VideoRectangle` + `Labels` control tags.
+
+More info: https://docs.ultralytics.com/modes/track/
+
+![Video Object Tracking](./YoloVideo.gif)
+
+
+https://github.com/user-attachments/assets/7b0d50e6-164a-4d66-87cf-df443b77f638
+
+
+
+### Labeling config
+
+```xml
+<View>
+    <Video name="video" value="$video"/>
+    <VideoRectangle name="box" toName="video" model_tracker="botsort" model_conf="0.25" model_iou="0.7" />
+    <Labels name="label" toName="video">
+      <Label value="Person" background="red"/>
+      <Label value="Car" background="blue"/>
+    </Labels>
+</View>
+```
+
+### Trackers
+
+https://docs.ultralytics.com/modes/track/?h=track#tracker-selection
+
+The best tracker to use with Ultralytics YOLO depends on your specific needs.
+
+
+The default tracker is [BoT-SORT](https://github.com/NirAharon/BoT-SORT), which is generally well-suited for most scenarios. 
+
+However, if you're looking for an alternative with different strengths, 
+[ByteTrack](https://github.com/ifzhang/ByteTrack) is another good choice that you can easily configure. 
+ByteTrack is known for its high performance in multi-object tracking, 
+especially in situations with varying object appearances and reappearances. 
+
+Both trackers can be customized using YAML configuration files to fit your specific use cases.
+
+You can specify the tracker in the control tag: 
+* `<VideoRectangle model_tracker="botsort">`
+* `<VideoRectangle model_tracker="bytetrack">`
+
+### Parameters for bounding boxes
+
+The tracker works with the object detection model (bounding boxes). 
+
+The first step is to detect bounding boxes, the second step is to track them (find the same boxes among frames). 
+These parameters are related to the first step - bounding box detection.
+
+Read more about these parameters: 
+https://docs.ultralytics.com/modes/track/?h=track#tracking-arguments
+
+| Parameter       | Type   | Default   | Description                                                                                                                                                                            |
+|-----------------|--------|-----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `model_conf`    | float  | 0.25      | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. |
+| `model_iou`     | float  | 0.7       | Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates.   |
+| `model_tracker` | string | `botsort` | Sets the tracker to use for multi-object tracking. Options include `botsort`, `bytetrack`, or a custom YAML file.                                                                      |
+| `model_path`    | string | None      | Path to the custom YOLO model. See more in the section "Custom YOLO Models."                                                                                                               |
+
+For example: 
+```xml
+<VideoRectangle name="label" toName="video" model_tracker="botsort" model_conf="0.25" model_iou="0.7" />  
+```
+
+### Parameters for trackers 
+
+For an example of tracker parameters, see https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers. 
+
+The main parameter is `model_tracker` which can be set to 
+* `botsort` 
+* `bytetrack`
+* Or the name of any custom yaml file that you place into `models` directory (do not include the file extension `.yaml` when setting this parameter).  
+
+As long as they are available within the yaml file, you can specify tracker parameters directly from the labeling config. All parameters should be prefixed with `botsort_` or `bytetrack_`.
+
+For example: 
+```xml
+<VideoRectangle 
+    name="label" toName="video" 
+    model_tracker="botsort" 
+    botsort_track_low_thresh="0.1" botsort_track_high_thresh="0.2" 
+/>  
+```
+
+### Default model
+
+`yolov8n.pt` is the default object detection model. 
+
+### Recommendations
+
+* Video object tracking is a computationally intensive task. 
+Small models like `yolov8n.pt` are recommended for real-time tracking, however, they may not be as accurate as larger models.
+
+* Label Studio has timeout limits for ML backend requests. You can adjust the timeout in the Label Studio backend settings.
+
+<!-- TODO: https://github.com/HumanSignal/label-studio/pull/5414/files#diff-20432d8093df2c0400b0f41b004a6b772b856b985fa1f5fd1e1f909247c89fc6L30 -->
+
+* Or use the [CLI tool](#When-to-use-the-CLI) to run predictions asynchronously.
+
+<br>
+
+-------------------
+
+<br>
+
+## Run the YOLO ML backend
+
+
+### Running with Docker (recommended)
+
+1. Start the Machine Learning backend on `http://localhost:9090` with the prebuilt image:
+
+    ```bash
+    docker-compose up
+    ```
+
+2. Validate that the backend is running
+
+    ```bash
+    $ curl http://localhost:9090/
+    {"status":"UP"}
+    ```
+
+3. Create a project in Label Studio. Then from the **Model** page in the project settings, [connect the model](https://labelstud.io/guide/ml#Connect-the-model-to-Label-Studio). The default URL is `http://localhost:9090`.
+
+### Building from source (advanced)
+
+To build the ML backend from source, you have to clone the repository and build the Docker image:
+
+```bash
+docker-compose build
+```
+
+### Running without Docker (advanced)
+
+To run the ML backend without Docker, you have to clone the repository and install all dependencies using pip:
+
+```bash
+python -m venv ml-backend
+source ml-backend/bin/activate
+pip install -r requirements-base.txt
+pip install -r requirements.txt
+```
+
+Then you can start the ML backend:
+
+```bash
+label-studio-ml start ./dir_with_your_model
+```
+
+Also, you can check [Dockerfile](Dockerfile) for additional dependencies and install them manually. 
+
+### Parameters
+
+Check the `environment` section in the [`docker-compose.yml`](docker-compose.yml) file before running the container. 
+All available parameters are listed there.
+
+> Note: You can use lots of YOLO model parameters in labeling configurations directly, e.g. `model_path` or `model_score_threshold`.
+
+## Command line interface for the terminal
+
+### Overview
+
+This Command Line Interface (CLI) tool facilitates the integration of YOLO models with Label Studio for machine learning predictions. 
+It provides an alternative method for running YOLO predictions on tasks managed by Label Studio, 
+particularly useful for processing long videos or large datasets. 
+
+Running the model predictions directly from the CLI helps to avoid issues 
+like connection timeouts between Label Studio and the ML backend, 
+which can occur during lengthy processing times.
+
+### When to use the CLI
+
+When working with extensive media files such as long videos, processing times can be significant. 
+Label Studio may interrupt the connection with the ML backend if the request takes too long, resulting in incomplete predictions. 
+
+By running this CLI tool, you can execute model predictions asynchronously 
+without the need for Label Studio to maintain a constant connection to the backend. 
+This ensures that even large or complex tasks are processed fully, 
+and predictions are saved to Label Studio using SDK once completed.
+
+### How it works
+
+1. **Label Studio Connection**: The tool connects to a running instance of Label Studio using the provided API key and URL.
+2. **Task Preparation**: Tasks can be provided directly via a JSON file or as a list of task IDs. The tool fetches task data from Label Studio if task IDs are supplied.
+3. **Model Loading**: The YOLO model is loaded and initialized based on the project’s configuration.
+4. **Prediction Process**: For each task, the YOLO model generates predictions, which are then post-processed to Label Studio's expected format.
+5. **Asynchronous Upload**: The generated predictions are uploaded back to Label Studio, allowing for large tasks to be processed without timing out.
+
+### Usage
+
+```bash
+python cli.py --ls-url http://localhost:8080 --ls-api-key your_api_key --project 1 --tasks tasks.json
+```
+
+or 
+
+```bash
+python cli.py --ls-url http://localhost:8080 --ls-api-key YOUR_API_KEY --project 1 --tasks 1,2,3
+```
+
+### Parameters
+
+- **`--ls-url`**: The URL of the Label Studio instance. Defaults to `http://localhost:8080`.
+- **`--ls-api-key`**: The API key for Label Studio. Used to authenticate the connection.
+- **`--project`**: The ID of the Label Studio project where the tasks are managed. Defaults to `1`.
+- **`--tasks`**:
+
+  1. The path to a JSON file containing a list of tasks or task IDs, e.g.:
+
+    tasks_ids.json 
+    ```json
+    [1,2,3]
+    ```
+  
+    tasks.json
+    ```json
+    [{"id": 1, "data": {"image": "https://example.com/1.jpg"}}, {"id": 2, "data": {"image": "https://example.com/2.jpg"}}]
+    ```
+  
+  2. If a file is not provided, you can pass a comma-separated list of task IDs directly, e.g.: `1,2,3`
+
+### Logging
+
+Use `LOG_LEVEL=DEBUG` to get detailed logs. Example:
+
+```bash
+LOG_LEVEL=DEBUG python cli.py --ls-url http://localhost:8080 --ls-api-key YOUR_API_KEY --project 2 --tasks 1,2,3
+```
+
+## For developers
+
+The architecture of the project and development guidelines are described in the [README_DEVELOP.md](README_DEVELOP.md) file. 
diff --git a/label_studio_ml/examples/yolo/README_DEVELOP.md b/label_studio_ml/examples/yolo/README_DEVELOP.md
new file mode 100644
index 000000000..1429d53df
--- /dev/null
+++ b/label_studio_ml/examples/yolo/README_DEVELOP.md
@@ -0,0 +1,131 @@
+```mermaid
+classDiagram
+    class ControlModel {
+        +str type
+        +ControlTag control
+        +str from_name
+        +str to_name
+        +str value
+        +YOLO model
+        +float model_score_threshold
+        +Optional[Dict[str, str]] label_map
+        +LabelStudioMLBase label_studio_ml_backend
+        +get_cached_model(path: str) YOLO
+        +create(cls, mlbackend: LabelStudioMLBase, control: ControlTag) ControlModel
+        +predict_regions(path: str) List[Dict]
+        +debug_plot(image)
+    }
+
+    class RectangleLabelsModel {
+        +predict_regions(path: str) List[Dict]
+        +create_rectangles(results, path) List[Dict]
+    }
+
+    class RectangleLabelsObbModel {
+        +predict_regions(path: str) List[Dict]
+        +create_rotated_rectangles(results, path) List[Dict]
+    }    
+    
+
+    class PolygonLabelsModel {
+        +predict_regions(path: str) List[Dict]
+        +create_polygons(results, path) List[Dict]
+    }
+    
+    class KeyPointLabelsModel {
+        +predict_regions(path: str) List[Dict]
+        +create_keypoints(results, path) List[Dict]
+    }
+
+    class ChoicesModel {
+        +predict_regions(path: str) List[Dict]
+        +create_choices(results, path) List[Dict]
+    }
+
+    class VideoRectangleModel {
+        +predict_regions(path: str) List[Dict]
+        +create_video_rectangles(results, path) List[Dict]
+        +update_tracker_params(yaml_path: str, prefix: str) str | None
+    }
+
+    ControlModel <|-- RectangleLabelsModel
+    ControlModel <|-- RectangleLabelsObbModel
+    ControlModel <|-- PolygonLabelsModel
+    ControlModel <|-- ChoicesModel
+    ControlModel <|-- KeyPointLabelsModel
+    ControlModel <|-- VideoRectangleModel
+    
+```
+
+### 1. **Architecture Overview**
+
+The architecture of the project is modular and is primarily centered around integrating YOLO-based models with Label Studio to automate the labeling of images and videos. The system is organized into several Python modules that interact with each other to perform this task. The main components of the architecture include:
+
+1. **Main YOLO Integration Module (`model.py`)**:
+   - This is the central module that connects Label Studio with YOLO models. It handles the overall process of detecting control tags from Label Studio’s configuration, running predictions on tasks, and returning the predictions in the format that Label Studio expects.
+
+2. **Control Models (`control_models/`)**:
+   - The control models are specialized modules that correspond to different annotation types in Label Studio (e.g., RectangleLabels, PolygonLabels, Choices, VideoRectangle). Each control model is responsible for handling specific types of annotations by using the YOLO model to predict the necessary regions or labels.
+
+3. **Base Control Model (`control_models/base.py`)**:
+   - This is an abstract base class that provides common functionality for all control models. It handles tasks like loading the YOLO model, caching it for efficiency, and providing a template for the predict and create methods.
+
+4. **Specific Control Models**:
+   - **RectangleLabelsModel (`control_models/rectanglelabels.py`)**: Handles bounding boxes (both simple and oriented bounding boxes) for images.
+   - **PolygonLabelsModel (`control_models/polygonlabels.py`)**: Deals with polygon annotations, typically used for segmentation tasks.
+   - **ChoicesModel (`control_models/choices.py`)**: Manages classification tasks where the model predicts one or more labels for the entire image.
+   - **KeyPointLabelsModel (`control_models/keypointlabels.py`)**: Supports keypoint annotations, where the model predicts the locations of keypoints on an image.
+   - **VideoRectangleModel (`control_models/videorectangle.py`)**: Focuses on tracking objects across video frames, generating bounding boxes for each frame.
+
+### 2. **Module Descriptions**
+
+1. **`model.py` (Main YOLO Integration Module)**:
+   - **Purpose**: This module serves as the entry point for integrating YOLO models with Label Studio. It is responsible for setting up the YOLO model, detecting which control models are needed based on the Label Studio configuration, running predictions on tasks, and returning the results in the required format.
+   - **Key Functions**:
+     - `setup()`: Initializes the YOLO model parameters.
+     - `detect_control_models()`: Scans the Label Studio configuration to determine which control models to use.
+     - `predict()`: Runs predictions on a batch of tasks and formats the results for Label Studio.
+     - `fit()`: (Not implemented) Placeholder for updating the model based on new annotations.
+
+2. **`control_models/base.py` (Base Control Model)**:
+   - **Purpose**: Provides a common interface and shared functionality for all specific control models. It includes methods for loading and caching the YOLO model, plotting results for debugging, and abstract methods that need to be implemented by subclasses.
+   - **Key Functions**:
+     - `get_cached_model()`: Retrieves a YOLO model from cache or loads it if not cached.
+     - `create()`: Factory method to instantiate a control model.
+     - `predict_regions()`: Abstract method to be implemented by subclasses to perform predictions.
+
+3. **`control_models/choices.py` (ChoicesModel)**:
+   - **Purpose**: Handles classification tasks where the model predicts one or more labels for an image. It converts the YOLO model’s classification output into Label Studio’s choices format.
+   - **Key Functions**:
+     - `create_choices()`: Processes the YOLO model’s output and maps it to the Label Studio choices format.
+
+4. **`control_models/rectanglelabels.py` (RectangleLabelsModel)**:
+   - **Purpose**: Manages the creation of bounding box annotations, both simple (axis-aligned) and oriented (rotated), from the YOLO model’s output.
+   - **Key Functions**:
+     - `create_rectangles()`: Converts the YOLO model’s bounding box predictions into Label Studio’s rectangle labels format.
+     - `create_rotated_rectangles()`: Handles oriented bounding boxes (OBB) by processing rotation angles and converting them to the required format.
+
+5. **`control_models/polygonlabels.py` (PolygonLabelsModel)**:
+   - **Purpose**: Converts segmentation masks generated by the YOLO model into polygon annotations for Label Studio. This is useful for tasks where precise boundaries around objects are required.
+   - **Key Functions**:
+     - `create_polygons()`: Transforms the YOLO model’s segmentation output into polygon annotations.
+
+6. **`control_models/keypointlabels.py` (KeyPointLabelsModel)**:
+   - **Purpose**: Supports keypoint annotations by predicting the locations of keypoints on an image using the pose YOLO model.
+   - **Key Functions**:
+     - `create_keypoints()`: Processes the YOLO model’s keypoint predictions and converts them into Label Studio’s keypoint labels format.
+
+7. **`control_models/videorectangle.py` (VideoRectangleModel)**:
+   - **Purpose**: Focuses on tracking objects across video frames, using YOLO’s tracking capabilities to generate bounding box annotations for each frame in a video sequence.
+   - **Key Functions**:
+     - `predict_regions()`: Runs YOLO’s tracking model on a video and converts the results into Label Studio’s video rectangle format.
+     - `create_video_rectangles()`: Processes the output of the tracking model to create a sequence of bounding boxes across video frames.
+     - `update_tracker_params()`: Customizes the tracking parameters based on settings in Label Studio’s configuration.
+
+### **Module Interaction**
+
+- **Workflow**: The main workflow begins with `model.py`, which reads tasks and the Label Studio configuration to detect and instantiate the appropriate control models. These control models are responsible for making predictions using the YOLO model and converting the results into a format that Label Studio can use for annotations.
+  
+- **Inter-Module Communication**: Each control model inherits from `ControlModel` in `base.py`, ensuring that they all share common methods for loading the YOLO model, handling predictions, and caching. The specific control models (e.g., RectangleLabelsModel, PolygonLabelsModel) implement the abstract methods defined in `ControlModel` to provide the specialized behavior needed for different types of annotations.
+
+This modular structure allows for easy extension and modification, where new control models can be added to handle additional annotation types or new model architectures.
diff --git a/label_studio_ml/examples/yolo/YOLO_CLASSES.md b/label_studio_ml/examples/yolo/YOLO_CLASSES.md
new file mode 100644
index 000000000..49db68a5d
--- /dev/null
+++ b/label_studio_ml/examples/yolo/YOLO_CLASSES.md
@@ -0,0 +1,16 @@
+# Label Names from YOLO v8 Models
+
+### Available "yolov8m.pt" model labels for RectangleLabels:
+['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
+
+### Available "yolov8n-obb.pt" model labels for RectangleLabels.OBB:** 
+['plane', 'ship', 'storage tank', 'baseball diamond', 'tennis court', 'basketball court', 'ground track field', 'harbor', 'bridge', 'large vehicle', 'small vehicle', 'helicopter', 'roundabout', 'soccer ball field', 'swimming pool']
+
+### Available "yolov8n-pose.pt" model labels for Keypoints: 
+['person']
+
+### Available "yolov8n-seg.pt" model labels for PolygonLabels: 
+['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
+
+### Available "yolov8n-cls.pt" model labels for Choices: 
+['tench', 'goldfish', 'great_white_shark', 'tiger_shark', 'hammerhead', 'electric_ray', 'stingray', 'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house_finch', 'junco', 'indigo_bunting', 'robin', 'bulbul', 'jay', 'magpie', 'chickadee', 'water_ouzel', 'kite', 'bald_eagle', 'vulture', 'great_grey_owl', 'European_fire_salamander', 'common_newt', 'eft', 'spotted_salamander', 'axolotl', 'bullfrog', 'tree_frog', 'tailed_frog', 'loggerhead', 'leatherback_turtle', 'mud_turtle', 'terrapin', 'box_turtle', 'banded_gecko', 'common_iguana', 'American_chameleon', 'whiptail', 'agama', 'frilled_lizard', 'alligator_lizard', 'Gila_monster', 'green_lizard', 'African_chameleon', 'Komodo_dragon', 'African_crocodile', 'American_alligator', 'triceratops', 'thunder_snake', 'ringneck_snake', 'hognose_snake', 'green_snake', 'king_snake', 'garter_snake', 'water_snake', 'vine_snake', 'night_snake', 'boa_constrictor', 'rock_python', 'Indian_cobra', 'green_mamba', 'sea_snake', 'horned_viper', 'diamondback', 'sidewinder', 'trilobite', 'harvestman', 'scorpion', 'black_and_gold_garden_spider', 'barn_spider', 'garden_spider', 'black_widow', 'tarantula', 'wolf_spider', 'tick', 'centipede', 'black_grouse', 'ptarmigan', 'ruffed_grouse', 'prairie_chicken', 'peacock', 'quail', 'partridge', 'African_grey', 'macaw', 'sulphur-crested_cockatoo', 'lorikeet', 'coucal', 'bee_eater', 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'drake', 'red-breasted_merganser', 'goose', 'black_swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea_anemone', 'brain_coral', 'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea_slug', 'chiton', 'chambered_nautilus', 'Dungeness_crab', 'rock_crab', 'fiddler_crab', 'king_crab', 'American_lobster', 'spiny_lobster', 'crayfish', 'hermit_crab', 'isopod', 'white_stork', 'black_stork', 'spoonbill', 'flamingo', 'little_blue_heron', 'American_egret', 'bittern', 'crane_(bird)', 'limpkin', 'European_gallinule', 'American_coot', 'bustard', 'ruddy_turnstone', 'red-backed_sandpiper', 'redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king_penguin', 'albatross', 'grey_whale', 'killer_whale', 'dugong', 'sea_lion', 'Chihuahua', 'Japanese_spaniel', 'Maltese_dog', 'Pekinese', 'Shih-Tzu', 'Blenheim_spaniel', 'papillon', 'toy_terrier', 'Rhodesian_ridgeback', 'Afghan_hound', 'basset', 'beagle', 'bloodhound', 'bluetick', 'black-and-tan_coonhound', 'Walker_hound', 'English_foxhound', 'redbone', 'borzoi', 'Irish_wolfhound', 'Italian_greyhound', 'whippet', 'Ibizan_hound', 'Norwegian_elkhound', 'otterhound', 'Saluki', 'Scottish_deerhound', 'Weimaraner', 'Staffordshire_bullterrier', 'American_Staffordshire_terrier', 'Bedlington_terrier', 'Border_terrier', 'Kerry_blue_terrier', 'Irish_terrier', 'Norfolk_terrier', 'Norwich_terrier', 'Yorkshire_terrier', 'wire-haired_fox_terrier', 'Lakeland_terrier', 'Sealyham_terrier', 'Airedale', 'cairn', 'Australian_terrier', 'Dandie_Dinmont', 'Boston_bull', 'miniature_schnauzer', 'giant_schnauzer', 'standard_schnauzer', 'Scotch_terrier', 'Tibetan_terrier', 'silky_terrier', 'soft-coated_wheaten_terrier', 'West_Highland_white_terrier', 'Lhasa', 'flat-coated_retriever', 'curly-coated_retriever', 'golden_retriever', 'Labrador_retriever', 'Chesapeake_Bay_retriever', 'German_short-haired_pointer', 'vizsla', 'English_setter', 'Irish_setter', 'Gordon_setter', 'Brittany_spaniel', 'clumber', 'English_springer', 'Welsh_springer_spaniel', 'cocker_spaniel', 'Sussex_spaniel', 'Irish_water_spaniel', 'kuvasz', 'schipperke', 'groenendael', 'malinois', 'briard', 'kelpie', 'komondor', 'Old_English_sheepdog', 'Shetland_sheepdog', 'collie', 'Border_collie', 'Bouvier_des_Flandres', 'Rottweiler', 'German_shepherd', 'Doberman', 'miniature_pinscher', 'Greater_Swiss_Mountain_dog', 'Bernese_mountain_dog', 'Appenzeller', 'EntleBucher', 'boxer', 'bull_mastiff', 'Tibetan_mastiff', 'French_bulldog', 'Great_Dane', 'Saint_Bernard', 'Eskimo_dog', 'malamute', 'Siberian_husky', 'dalmatian', 'affenpinscher', 'basenji', 'pug', 'Leonberg', 'Newfoundland', 'Great_Pyrenees', 'Samoyed', 'Pomeranian', 'chow', 'keeshond', 'Brabancon_griffon', 'Pembroke', 'Cardigan', 'toy_poodle', 'miniature_poodle', 'standard_poodle', 'Mexican_hairless', 'timber_wolf', 'white_wolf', 'red_wolf', 'coyote', 'dingo', 'dhole', 'African_hunting_dog', 'hyena', 'red_fox', 'kit_fox', 'Arctic_fox', 'grey_fox', 'tabby', 'tiger_cat', 'Persian_cat', 'Siamese_cat', 'Egyptian_cat', 'cougar', 'lynx', 'leopard', 'snow_leopard', 'jaguar', 'lion', 'tiger', 'cheetah', 'brown_bear', 'American_black_bear', 'ice_bear', 'sloth_bear', 'mongoose', 'meerkat', 'tiger_beetle', 'ladybug', 'ground_beetle', 'long-horned_beetle', 'leaf_beetle', 'dung_beetle', 'rhinoceros_beetle', 'weevil', 'fly', 'bee', 'ant', 'grasshopper', 'cricket', 'walking_stick', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly', 'damselfly', 'admiral', 'ringlet', 'monarch', 'cabbage_butterfly', 'sulphur_butterfly', 'lycaenid', 'starfish', 'sea_urchin', 'sea_cucumber', 'wood_rabbit', 'hare', 'Angora', 'hamster', 'porcupine', 'fox_squirrel', 'marmot', 'beaver', 'guinea_pig', 'sorrel', 'zebra', 'hog', 'wild_boar', 'warthog', 'hippopotamus', 'ox', 'water_buffalo', 'bison', 'ram', 'bighorn', 'ibex', 'hartebeest', 'impala', 'gazelle', 'Arabian_camel', 'llama', 'weasel', 'mink', 'polecat', 'black-footed_ferret', 'otter', 'skunk', 'badger', 'armadillo', 'three-toed_sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon', 'siamang', 'guenon', 'patas', 'baboon', 'macaque', 'langur', 'colobus', 'proboscis_monkey', 'marmoset', 'capuchin', 'howler_monkey', 'titi', 'spider_monkey', 'squirrel_monkey', 'Madagascar_cat', 'indri', 'Indian_elephant', 'African_elephant', 'lesser_panda', 'giant_panda', 'barracouta', 'eel', 'coho', 'rock_beauty', 'anemone_fish', 'sturgeon', 'gar', 'lionfish', 'puffer', 'abacus', 'abaya', 'academic_gown', 'accordion', 'acoustic_guitar', 'aircraft_carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibian', 'analog_clock', 'apiary', 'apron', 'ashcan', 'assault_rifle', 'backpack', 'bakery', 'balance_beam', 'balloon', 'ballpoint', 'Band_Aid', 'banjo', 'bannister', 'barbell', 'barber_chair', 'barbershop', 'barn', 'barometer', 'barrel', 'barrow', 'baseball', 'basketball', 'bassinet', 'bassoon', 'bathing_cap', 'bath_towel', 'bathtub', 'beach_wagon', 'beacon', 'beaker', 'bearskin', 'beer_bottle', 'beer_glass', 'bell_cote', 'bib', 'bicycle-built-for-two', 'bikini', 'binder', 'binoculars', 'birdhouse', 'boathouse', 'bobsled', 'bolo_tie', 'bonnet', 'bookcase', 'bookshop', 'bottlecap', 'bow', 'bow_tie', 'brass', 'brassiere', 'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof_vest', 'bullet_train', 'butcher_shop', 'cab', 'caldron', 'candle', 'cannon', 'canoe', 'can_opener', 'cardigan', 'car_mirror', 'carousel', "carpenter's_kit", 'carton', 'car_wheel', 'cash_machine', 'cassette', 'cassette_player', 'castle', 'catamaran', 'CD_player', 'cello', 'cellular_telephone', 'chain', 'chainlink_fence', 'chain_mail', 'chain_saw', 'chest', 'chiffonier', 'chime', 'china_cabinet', 'Christmas_stocking', 'church', 'cinema', 'cleaver', 'cliff_dwelling', 'cloak', 'clog', 'cocktail_shaker', 'coffee_mug', 'coffeepot', 'coil', 'combination_lock', 'computer_keyboard', 'confectionery', 'container_ship', 'convertible', 'corkscrew', 'cornet', 'cowboy_boot', 'cowboy_hat', 'cradle', 'crane_(machine)', 'crash_helmet', 'crate', 'crib', 'Crock_Pot', 'croquet_ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop_computer', 'dial_telephone', 'diaper', 'digital_clock', 'digital_watch', 'dining_table', 'dishrag', 'dishwasher', 'disk_brake', 'dock', 'dogsled', 'dome', 'doormat', 'drilling_platform', 'drum', 'drumstick', 'dumbbell', 'Dutch_oven', 'electric_fan', 'electric_guitar', 'electric_locomotive', 'entertainment_center', 'envelope', 'espresso_maker', 'face_powder', 'feather_boa', 'file', 'fireboat', 'fire_engine', 'fire_screen', 'flagpole', 'flute', 'folding_chair', 'football_helmet', 'forklift', 'fountain', 'fountain_pen', 'four-poster', 'freight_car', 'French_horn', 'frying_pan', 'fur_coat', 'garbage_truck', 'gasmask', 'gas_pump', 'goblet', 'go-kart', 'golf_ball', 'golfcart', 'gondola', 'gong', 'gown', 'grand_piano', 'greenhouse', 'grille', 'grocery_store', 'guillotine', 'hair_slide', 'hair_spray', 'half_track', 'hammer', 'hamper', 'hand_blower', 'hand-held_computer', 'handkerchief', 'hard_disc', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home_theater', 'honeycomb', 'hook', 'hoopskirt', 'horizontal_bar', 'horse_cart', 'hourglass', 'iPod', 'iron', "jack-o'-lantern", 'jean', 'jeep', 'jersey', 'jigsaw_puzzle', 'jinrikisha', 'joystick', 'kimono', 'knee_pad', 'knot', 'lab_coat', 'ladle', 'lampshade', 'laptop', 'lawn_mower', 'lens_cap', 'letter_opener', 'library', 'lifeboat', 'lighter', 'limousine', 'liner', 'lipstick', 'Loafer', 'lotion', 'loudspeaker', 'loupe', 'lumbermill', 'magnetic_compass', 'mailbag', 'mailbox', 'maillot_(tights)', 'maillot_(tank_suit)', 'manhole_cover', 'maraca', 'marimba', 'mask', 'matchstick', 'maypole', 'maze', 'measuring_cup', 'medicine_chest', 'megalith', 'microphone', 'microwave', 'military_uniform', 'milk_can', 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing_bowl', 'mobile_home', 'Model_T', 'modem', 'monastery', 'monitor', 'moped', 'mortar', 'mortarboard', 'mosque', 'mosquito_net', 'motor_scooter', 'mountain_bike', 'mountain_tent', 'mouse', 'mousetrap', 'moving_van', 'muzzle', 'nail', 'neck_brace', 'necklace', 'nipple', 'notebook', 'obelisk', 'oboe', 'ocarina', 'odometer', 'oil_filter', 'organ', 'oscilloscope', 'overskirt', 'oxcart', 'oxygen_mask', 'packet', 'paddle', 'paddlewheel', 'padlock', 'paintbrush', 'pajama', 'palace', 'panpipe', 'paper_towel', 'parachute', 'parallel_bars', 'park_bench', 'parking_meter', 'passenger_car', 'patio', 'pay-phone', 'pedestal', 'pencil_box', 'pencil_sharpener', 'perfume', 'Petri_dish', 'photocopier', 'pick', 'pickelhaube', 'picket_fence', 'pickup', 'pier', 'piggy_bank', 'pill_bottle', 'pillow', 'ping-pong_ball', 'pinwheel', 'pirate', 'pitcher', 'plane', 'planetarium', 'plastic_bag', 'plate_rack', 'plow', 'plunger', 'Polaroid_camera', 'pole', 'police_van', 'poncho', 'pool_table', 'pop_bottle', 'pot', "potter's_wheel", 'power_drill', 'prayer_rug', 'printer', 'prison', 'projectile', 'projector', 'puck', 'punching_bag', 'purse', 'quill', 'quilt', 'racer', 'racket', 'radiator', 'radio', 'radio_telescope', 'rain_barrel', 'recreational_vehicle', 'reel', 'reflex_camera', 'refrigerator', 'remote_control', 'restaurant', 'revolver', 'rifle', 'rocking_chair', 'rotisserie', 'rubber_eraser', 'rugby_ball', 'rule', 'running_shoe', 'safe', 'safety_pin', 'saltshaker', 'sandal', 'sarong', 'sax', 'scabbard', 'scale', 'school_bus', 'schooner', 'scoreboard', 'screen', 'screw', 'screwdriver', 'seat_belt', 'sewing_machine', 'shield', 'shoe_shop', 'shoji', 'shopping_basket', 'shopping_cart', 'shovel', 'shower_cap', 'shower_curtain', 'ski', 'ski_mask', 'sleeping_bag', 'slide_rule', 'sliding_door', 'slot', 'snorkel', 'snowmobile', 'snowplow', 'soap_dispenser', 'soccer_ball', 'sock', 'solar_dish', 'sombrero', 'soup_bowl', 'space_bar', 'space_heater', 'space_shuttle', 'spatula', 'speedboat', 'spider_web', 'spindle', 'sports_car', 'spotlight', 'stage', 'steam_locomotive', 'steel_arch_bridge', 'steel_drum', 'stethoscope', 'stole', 'stone_wall', 'stopwatch', 'stove', 'strainer', 'streetcar', 'stretcher', 'studio_couch', 'stupa', 'submarine', 'suit', 'sundial', 'sunglass', 'sunglasses', 'sunscreen', 'suspension_bridge', 'swab', 'sweatshirt', 'swimming_trunks', 'swing', 'switch', 'syringe', 'table_lamp', 'tank', 'tape_player', 'teapot', 'teddy', 'television', 'tennis_ball', 'thatch', 'theater_curtain', 'thimble', 'thresher', 'throne', 'tile_roof', 'toaster', 'tobacco_shop', 'toilet_seat', 'torch', 'totem_pole', 'tow_truck', 'toyshop', 'tractor', 'trailer_truck', 'tray', 'trench_coat', 'tricycle', 'trimaran', 'tripod', 'triumphal_arch', 'trolleybus', 'trombone', 'tub', 'turnstile', 'typewriter_keyboard', 'umbrella', 'unicycle', 'upright', 'vacuum', 'vase', 'vault', 'velvet', 'vending_machine', 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle_iron', 'wall_clock', 'wallet', 'wardrobe', 'warplane', 'washbasin', 'washer', 'water_bottle', 'water_jug', 'water_tower', 'whiskey_jug', 'whistle', 'wig', 'window_screen', 'window_shade', 'Windsor_tie', 'wine_bottle', 'wing', 'wok', 'wooden_spoon', 'wool', 'worm_fence', 'wreck', 'yawl', 'yurt', 'web_site', 'comic_book', 'crossword_puzzle', 'street_sign', 'traffic_light', 'book_jacket', 'menu', 'plate', 'guacamole', 'consomme', 'hot_pot', 'trifle', 'ice_cream', 'ice_lolly', 'French_loaf', 'bagel', 'pretzel', 'cheeseburger', 'hotdog', 'mashed_potato', 'head_cabbage', 'broccoli', 'cauliflower', 'zucchini', 'spaghetti_squash', 'acorn_squash', 'butternut_squash', 'cucumber', 'artichoke', 'bell_pepper', 'cardoon', 'mushroom', 'Granny_Smith', 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard_apple', 'pomegranate', 'hay', 'carbonara', 'chocolate_sauce', 'dough', 'meat_loaf', 'pizza', 'potpie', 'burrito', 'red_wine', 'espresso', 'cup', 'eggnog', 'alp', 'bubble', 'cliff', 'coral_reef', 'geyser', 'lakeside', 'promontory', 'sandbar', 'seashore', 'valley', 'volcano', 'ballplayer', 'groom', 'scuba_diver', 'rapeseed', 'daisy', "yellow_lady's_slipper", 'corn', 'acorn', 'hip', 'buckeye', 'coral_fungus', 'agaric', 'gyromitra', 'stinkhorn', 'earthstar', 'hen-of-the-woods', 'bolete', 'ear', 'toilet_tissue']
diff --git a/label_studio_ml/examples/yolo/__init__.py b/label_studio_ml/examples/yolo/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/label_studio_ml/examples/yolo/_wsgi.py b/label_studio_ml/examples/yolo/_wsgi.py
new file mode 100644
index 000000000..c43cc9003
--- /dev/null
+++ b/label_studio_ml/examples/yolo/_wsgi.py
@@ -0,0 +1,145 @@
+import os
+import argparse
+import json
+import logging
+import logging.config
+
+logging.config.dictConfig(
+    {
+        "version": 1,
+        "formatters": {
+            "standard": {
+                "format": "[%(asctime)s] [%(levelname)s] [%(name)s::%(funcName)s::%(lineno)d] %(message)s"
+            }
+        },
+        "handlers": {
+            "console": {
+                "class": "logging.StreamHandler",
+                "level": os.getenv("LOG_LEVEL"),
+                "stream": "ext://sys.stdout",
+                "formatter": "standard",
+            }
+        },
+        "root": {
+            "level": os.getenv("LOG_LEVEL"),
+            "handlers": ["console"],
+            "propagate": True,
+        },
+    }
+)
+
+from label_studio_ml.api import init_app
+from model import YOLO
+
+
+_DEFAULT_CONFIG_PATH = os.path.join(os.path.dirname(__file__), "config.json")
+
+
+def get_kwargs_from_config(config_path=_DEFAULT_CONFIG_PATH):
+    if not os.path.exists(config_path):
+        return dict()
+    with open(config_path) as f:
+        config = json.load(f)
+    assert isinstance(config, dict)
+    return config
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Label studio")
+    parser.add_argument(
+        "-p", "--port", dest="port", type=int, default=9090, help="Server port"
+    )
+    parser.add_argument(
+        "--host", dest="host", type=str, default="0.0.0.0", help="Server host"
+    )
+    parser.add_argument(
+        "--kwargs",
+        "--with",
+        dest="kwargs",
+        metavar="KEY=VAL",
+        nargs="+",
+        type=lambda kv: kv.split("="),
+        help="Additional LabelStudioMLBase model initialization kwargs",
+    )
+    parser.add_argument(
+        "-d", "--debug", dest="debug", action="store_true", help="Switch debug mode"
+    )
+    parser.add_argument(
+        "--log-level",
+        dest="log_level",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
+        default=None,
+        help="Logging level",
+    )
+    parser.add_argument(
+        "--model-dir",
+        dest="model_dir",
+        default=os.path.dirname(__file__),
+        help="Directory where models are stored (relative to the project directory)",
+    )
+    parser.add_argument(
+        "--check",
+        dest="check",
+        action="store_true",
+        help="Validate model instance before launching server",
+    )
+    parser.add_argument(
+        "--basic-auth-user",
+        default=os.environ.get("ML_SERVER_BASIC_AUTH_USER", None),
+        help="Basic auth user",
+    )
+
+    parser.add_argument(
+        "--basic-auth-pass",
+        default=os.environ.get("ML_SERVER_BASIC_AUTH_PASS", None),
+        help="Basic auth pass",
+    )
+
+    args = parser.parse_args()
+
+    # setup logging level
+    if args.log_level:
+        logging.root.setLevel(args.log_level)
+
+    def isfloat(value):
+        try:
+            float(value)
+            return True
+        except ValueError:
+            return False
+
+    def parse_kwargs():
+        param = dict()
+        for k, v in args.kwargs:
+            if v.isdigit():
+                param[k] = int(v)
+            elif v == "True" or v == "true":
+                param[k] = True
+            elif v == "False" or v == "false":
+                param[k] = False
+            elif isfloat(v):
+                param[k] = float(v)
+            else:
+                param[k] = v
+        return param
+
+    kwargs = get_kwargs_from_config()
+
+    if args.kwargs:
+        kwargs.update(parse_kwargs())
+
+    if args.check:
+        print('Check "' + YOLO.__name__ + '" instance creation..')
+        model = YOLO(**kwargs)
+
+    app = init_app(
+        model_class=YOLO,
+        basic_auth_user=args.basic_auth_user,
+        basic_auth_pass=args.basic_auth_pass,
+    )
+
+    app.run(host=args.host, port=args.port, debug=args.debug)
+
+else:
+    # for uWSGI use
+    app = init_app(model_class=YOLO)
diff --git a/label_studio_ml/examples/yolo/cli.py b/label_studio_ml/examples/yolo/cli.py
new file mode 100644
index 000000000..10df0c93f
--- /dev/null
+++ b/label_studio_ml/examples/yolo/cli.py
@@ -0,0 +1,139 @@
+import os
+import logging
+import json
+
+from tqdm import tqdm
+from argparse import ArgumentParser
+from model import YOLO
+from label_studio_sdk.client import LabelStudio
+from label_studio_ml.response import ModelResponse
+
+
+LABEL_STUDIO_URL = os.getenv("LABEL_STUDIO_URL", "http://localhost:8080")
+LABEL_STUDIO_API_KEY = os.getenv("LABEL_STUDIO_API_KEY", "your_api_key")
+PROJECT_ID = os.getenv("LABEL_STUDIO_PROJECT_ID", "1")
+
+logger = logging.getLogger(__name__)
+
+
+def arg_parser():
+    parser = ArgumentParser(description="YOLO client for Label Studio ML Backend")
+
+    parser.add_argument(
+        "--ls-url", type=str, default=LABEL_STUDIO_URL, help="Label Studio URL"
+    )
+    parser.add_argument(
+        "--ls-api-key",
+        type=str,
+        default=LABEL_STUDIO_API_KEY,
+        help="Label Studio API Key",
+    )
+    parser.add_argument(
+        "--project", type=str, default="1", help="Label Studio Project ID"
+    )
+    parser.add_argument(
+        "--tasks",
+        type=str,
+        default="tasks.json",
+        help="Path to tasks JSON file with list of ids or task datas. Example: tasks.json\n"
+        "String with ids separated by comma: if you provide task ids, "
+        "task data will be downloaded automatically from the Label Studio instance. Example: 1,2,3",
+    )
+    return parser.parse_args()
+
+
+class LabelStudioMLPredictor:
+    def __init__(self, ls_url, ls_api_key):
+        self.ls = LabelStudio(base_url=ls_url, api_key=ls_api_key)
+        logger.info(f"Successfully connected to Label Studio: {ls_url}")
+
+    def run(self, project, tasks):
+        # initialize Label Studio SDK client
+        ls = self.ls
+        project = ls.projects.get(id=project)
+        logger.info(f"Project is retrieved: {project.id}")
+
+        tasks = self.prepare_tasks(ls, tasks)
+
+        # load YOLO model
+        # TODO: use get_all_classes_inherited_LabelStudioMLBase to detect model classes
+        model = YOLO(project_id=project.id, label_config=project.label_config)
+        logger.info(f"YOLO ML backend is created")
+
+        # predict and send prediction to Label Studio
+        for task in tqdm(tasks, desc="Predict tasks"):
+            response = model.predict([task])
+            predictions = self.postprocess_response(model, response, task)
+
+            # send predictions to Label Studio
+            for prediction in predictions:
+                ls.predictions.create(
+                    task=task["id"],
+                    score=prediction.get("score", 0),
+                    model_version=prediction.get("model_version", "none"),
+                    result=prediction["result"],
+                )
+
+        logger.info("Model predictions are done!")
+
+    @staticmethod
+    def postprocess_response(model, response, task):
+        if response is None:
+            logger.warning(f"No predictions for task: {task}")
+            return None
+
+        # model returned ModelResponse
+        if isinstance(response, ModelResponse):
+            # check model version
+            if not response.has_model_version():
+                if model.model_version:
+                    response.set_version(str(model.model_version))
+            else:
+                response.update_predictions_version()
+            response = response.model_dump()
+            predictions = response.get("predictions")
+        # model returned list of dicts with predictions (old format)
+        elif isinstance(response, list):
+            predictions = response
+        else:
+            logger.error("No predictions generated by model")
+            return None
+
+        return predictions
+
+    @staticmethod
+    def prepare_tasks(ls, tasks):
+        # get tasks
+        if os.path.exists(tasks):
+            with open(tasks) as f:
+                tasks = json.load(f)
+        else:
+            tasks = tasks.split(",")
+            tasks = [int(task) for task in tasks]
+        assert isinstance(tasks, list), "Tasks should be a list"
+        assert len(tasks) > 0, "'Task list can't be empty"
+        logger.info(f"Detected {len(tasks)} tasks")
+        # check task data
+        if isinstance(tasks[0], dict):
+            if "data" not in tasks[0] or "id" not in tasks[0]:
+                raise ValueError("'data' and 'id' must be presented in all tasks")
+        elif isinstance(tasks[0], int):
+            # load tasks from Label Studio instance using SDK
+            logger.info("Task loading from Label Studio instance ...")
+            tasks = [
+                {"id": task_id, "data": ls.tasks.get(task_id).data}
+                for task_id in tqdm(tasks)
+            ]
+            logger.info("Task loading finished")
+        else:
+            raise ValueError(
+                "Unknown task format: "
+                "tasks should be a list of dicts (task data) or a list of task ids"
+            )
+        return tasks
+
+
+if __name__ == "__main__":
+    args = arg_parser()
+    predictor = LabelStudioMLPredictor(args.ls_url, args.ls_api_key)
+    predictor.run(args.project, args.tasks)
diff --git a/label_studio_ml/examples/yolo/control_models/__init__.py b/label_studio_ml/examples/yolo/control_models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/label_studio_ml/examples/yolo/control_models/base.py b/label_studio_ml/examples/yolo/control_models/base.py
new file mode 100644
index 000000000..0a9cdb3e1
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/base.py
@@ -0,0 +1,161 @@
+import os
+import logging
+
+from pydantic import BaseModel
+from typing import Optional, List, Dict, ClassVar
+from ultralytics import YOLO
+
+from label_studio_ml.model import LabelStudioMLBase
+from label_studio_sdk.label_interface.control_tags import ControlTag
+from label_studio_sdk.label_interface import LabelInterface
+
+
+# use matplotlib plots for debug
+DEBUG_PLOT = os.getenv("DEBUG_PLOT", "false").lower() in ["1", "true"]
+SCORE_THRESHOLD = float(os.getenv("SCORE_THRESHOLD", 0.5))
+DEFAULT_MODEL_ROOT = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
+MODEL_ROOT = os.getenv("MODEL_ROOT", DEFAULT_MODEL_ROOT)
+os.makedirs(MODEL_ROOT, exist_ok=True)
+# if true, allow to use custom model path from the control tag in the labeling config
+ALLOW_CUSTOM_MODEL_PATH = os.getenv("ALLOW_CUSTOM_MODEL_PATH", "true").lower() in [
+    "1",
+    "true",
+]
+
+# Global cache for YOLO models
+_model_cache = {}
+logger = logging.getLogger(__name__)
+
+
+class ControlModel(BaseModel):
+    """
+    Represents a control tag in Label Studio, which is associated with a specific type of labeling task
+    and is used to generate predictions using a YOLO model.
+
+    Attributes:
+        type (str): Type of the control, e.g., RectangleLabels, Choices, etc.
+        control (ControlTag): The actual control element from the Label Studio configuration.
+        from_name (str): The name of the control tag, used to link the control to the data.
+        to_name (str): The name of the data field that this control is associated with.
+        value (str): The value name from the object that this control operates on, e.g., an image or text field.
+        model (object): The model instance (e.g., YOLO) used to generate predictions for this control.
+        model_path (str): Path to the YOLO model file.
+        score_threshold (float): Threshold for prediction scores; predictions below this value will be ignored.
+        label_map (Optional[Dict[str, str]]): A mapping of model labels to Label Studio labels.
+    """
+
+    type: ClassVar[str]
+    control: ControlTag
+    from_name: str
+    to_name: str
+    value: str
+    model: YOLO
+    model_path: ClassVar[str]
+    score_threshold: float = 0.5
+    label_map: Optional[Dict[str, str]] = {}
+    label_studio_ml_backend: LabelStudioMLBase
+
+    def __init__(self, **data):
+        super().__init__(**data)
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        """Check if the control tag matches the model type.
+        Args:
+            control (ControlTag): The control tag from the Label Studio Interface.
+        """
+        raise NotImplementedError("This method should be overridden in derived classes")
+
+    @staticmethod
+    def get_from_name_for_label_map(
+        label_interface: LabelInterface, target_name: str
+    ) -> str:
+        """Get the 'from_name' attribute for the label map building."""
+        return target_name
+
+    @classmethod
+    def create(cls, mlbackend: LabelStudioMLBase, control: ControlTag):
+        """Factory method to create an instance of a specific control model class.
+        Args:
+            mlbackend (LabelStudioMLBase): The ML backend instance.
+            control (ControlTag): The control tag from the Label Studio Interface.
+        """
+        from_name = control.name
+        to_name = control.to_name[0]
+        value = control.objects[0].value_name
+
+        # if skip is true, don't process this control
+        if control.attr.get("model_skip", "false").lower() in ["1", "true", "yes"]:
+            logger.info(
+                f"Skipping control tag '{control.tag}' with name '{from_name}', model_skip=true found"
+            )
+            return None
+        # read `score_threshold` attribute from the control tag, e.g.: <RectangleLabels score_threshold="0.5">
+        score_threshold = float(control.attr.get("score_threshold") or SCORE_THRESHOLD)
+        # read `model_path` attribute from the control tag
+        model_path = (
+            ALLOW_CUSTOM_MODEL_PATH and control.attr.get("model_path")
+        ) or cls.model_path
+
+        model = cls.get_cached_model(model_path)
+        model_names = model.names.values()  # class names from the model
+        # from_name for label mapping can be differed from control.name (e.g. VideoRectangle)
+        label_map_from_name = cls.get_from_name_for_label_map(
+            mlbackend.label_interface, from_name
+        )
+        label_map = mlbackend.build_label_map(label_map_from_name, model_names)
+
+        return cls(
+            control=control,
+            from_name=from_name,
+            to_name=to_name,
+            value=value,
+            model=model,
+            score_threshold=score_threshold,
+            label_map=label_map,
+            label_studio_ml_backend=mlbackend,
+        )
+
+    @classmethod
+    def load_yolo_model(cls, filename) -> YOLO:
+        """Load YOLO model from the file."""
+        path = os.path.join(MODEL_ROOT, filename)
+        logger.info(f"Loading yolo model: {path}")
+        model = YOLO(path)
+        logger.info(f"Model {path} names:\n{model.names}")
+        return model
+
+    @classmethod
+    def get_cached_model(cls, path: str) -> YOLO:
+        if path not in _model_cache:
+            _model_cache[path] = cls.load_yolo_model(path)
+        return _model_cache[path]
+
+    def debug_plot(self, image):
+        if not DEBUG_PLOT:
+            return
+
+        import matplotlib.pyplot as plt
+
+        plt.figure(figsize=(10, 10))
+        plt.imshow(image[..., ::-1])
+        plt.axis("off")
+        plt.title(self.type)
+        plt.show()
+
+    def predict_regions(self, path) -> List[Dict]:
+        """Predict regions in the image using the YOLO model.
+        Args:
+            path (str): Path to the file with media
+        """
+        raise NotImplementedError("This method should be overridden in derived classes")
+
+    def __str__(self):
+        """Return a string with full representation of the control tag."""
+        return (
+            f"{self.type} from_name={self.from_name}, "
+            f"label_map={self.label_map}, score_threshold={self.score_threshold}"
+        )
+
+    class Config:
+        arbitrary_types_allowed = True
diff --git a/label_studio_ml/examples/yolo/control_models/choices.py b/label_studio_ml/examples/yolo/control_models/choices.py
new file mode 100644
index 000000000..83a809ce7
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/choices.py
@@ -0,0 +1,92 @@
+import logging
+import numpy as np
+
+from control_models.base import ControlModel
+from typing import List, Dict
+
+
+logger = logging.getLogger(__name__)
+
+
+class ChoicesModel(ControlModel):
+    """
+    Class representing a Choices (classes) control tag for YOLO model.
+    """
+
+    type = "Choices"
+    model_path = "yolov8n-cls.pt"
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        # check object tag type
+        if control.objects[0].tag != "Image":
+            return False
+        # support both Choices and Taxonomy because of their similarity
+        return control.tag in [cls.type, "Taxonomy"]
+
+    def predict_regions(self, path) -> List[Dict]:
+        results = self.model.predict(path)
+        self.debug_plot(results[0].plot())
+        return self.create_choices(results, path)
+
+    def create_choices(self, results, path):
+        logger.debug(f"create_choices: {self.from_name}")
+        mode = self.control.attr.get("choice", "single")
+        data = results[0].probs.numpy().data
+
+        # single
+        if mode in ["single", "single-radio"]:
+            # we must keep data items that matches label_map only, because we need to search among label_map only
+            indexes = [
+                i for i, name in self.model.names.items() if name in self.label_map
+            ]
+            data = data[indexes]
+            model_names = [self.model.names[i] for i in indexes]
+            # find the best choice
+            index = np.argmax(data)
+            probs = [data[index]]
+            names = [model_names[index]]
+        # multi
+        else:
+            # get indexes of data where data >= self.score_threshold
+            indexes = np.where(data >= self.score_threshold)
+            probs = data[indexes].tolist()
+            names = [self.model.names[int(i)] for i in indexes[0]]
+
+        if not probs:
+            logger.debug("No choices found")
+            return []
+
+        score = np.mean(probs)
+        logger.debug(
+            "----------------------\n"
+            f"task id > {path}\n"
+            f"control: {self.control}\n"
+            f"probs > {probs}\n"
+            f"score > {score}\n"
+            f"names > {names}\n"
+        )
+
+        if score < self.score_threshold:
+            logger.debug(f"Score is too low for single choice: {names[0]} = {probs[0]}")
+            return []
+
+        # map to Label Studio labels
+        output_labels = [
+            self.label_map[name] for name in names if name in self.label_map
+        ]
+
+        # add new region with rectangle
+        return [
+            {
+                "from_name": self.from_name,
+                "to_name": self.to_name,
+                "type": "choices",
+                "value": {"choices": output_labels},
+                "score": float(score),
+            }
+        ]
+
+
+# pre-load and cache default model at startup
+ChoicesModel.get_cached_model(ChoicesModel.model_path)
diff --git a/label_studio_ml/examples/yolo/control_models/keypoint_labels.py b/label_studio_ml/examples/yolo/control_models/keypoint_labels.py
new file mode 100644
index 000000000..122392f8e
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/keypoint_labels.py
@@ -0,0 +1,173 @@
+import logging
+from control_models.base import ControlModel
+from typing import List, Dict
+
+logger = logging.getLogger(__name__)
+
+
+class KeypointLabelsModel(ControlModel):
+    """
+    Class representing a KeypointLabels control tag for YOLO model.
+    """
+
+    type = "KeyPointLabels"
+    model_path = (
+        "yolov8n-pose.pt"  # Adjust the model path to your keypoint detection model
+    )
+    add_bboxes: bool = True
+    point_size: float = 1
+    point_threshold: float = 0
+    point_map: Dict = {}
+
+    def __init__(self, **data):
+        super().__init__(**data)
+
+        self.add_bboxes = self.control.attr.get("model_add_bboxes", "true").lower() in [
+            "1",
+            "true",
+            "yes",
+        ]
+        self.point_size = float(self.control.attr.get("model_point_size", 1))
+        self.point_threshold = float(self.control.attr.get("model_point_threshold", 0))
+        self.point_map = self.build_point_mapping()
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        # Check object tag type
+        if control.objects[0].tag != "Image":
+            return False
+        return control.tag == cls.type
+
+    def build_point_mapping(self):
+        """Build a mapping between points and Label Studio labels, e.g.
+        <Label value="left_eye" predicted_values="person" model_index="2" /> => {"person::2": "left_eye"}
+        """
+        mapping = {}
+        for value, label_tag in self.control.labels_attrs.items():
+            model_name = label_tag.attr.get("predicted_values")
+            model_index = label_tag.attr.get("model_index")
+            if model_name and not model_index:
+                logger.warning(
+                    f"`model_index` is not provided for Label tag: {label_tag}"
+                )
+            if not model_name and model_index:
+                logger.warning(
+                    f"`predicted_values` is not provided for Label tag: {label_tag}"
+                )
+            if model_name and model_index:
+                mapping[f"{model_name}::{model_index}"] = value
+
+        if not mapping:
+            logger.error(
+                f"No point to label mapping found for control tag: {self.control}"
+            )
+        return mapping
+
+    def predict_regions(self, path) -> List[Dict]:
+        results = self.model.predict(path)
+        return self.create_keypoints(results, path)
+
+    def create_keypoints(self, results, path):
+        logger.debug(f"create_keypoints: {self.from_name}")
+        keypoints_data = results[0].keypoints  # Get keypoints from the first frame
+        bbox_data = results[0].boxes
+        image_width = results[0].orig_shape[1]
+        regions = []
+
+        for bbox_index in range(
+            keypoints_data.shape[0]
+        ):  # Iterate over detected bboxes
+            bbox_conf = bbox_data.conf[bbox_index]
+            point_xyn = (
+                keypoints_data.xyn[bbox_index] * 100
+            )  # Convert normalized keypoints to percentages
+            model_label = self.model.names[int(results[0].boxes.cls[bbox_index])]
+
+            logger.debug(
+                "----------------------\n"
+                f"task id > {path}\n"
+                f"type: {self.control}\n"
+                f"keypoints > {point_xyn}\n"
+                f"model label > {model_label}\n"
+                f"confidences > {bbox_conf}\n"
+            )
+
+            # bbox score is too low
+            if bbox_conf < self.score_threshold:
+                continue
+
+            # There is no mapping between model label and LS label
+            if model_label not in self.label_map:
+                continue
+
+            # Add parent bbox that contains all keypoints
+            if self.add_bboxes:
+                region = self.create_bounding_box(
+                    bbox_conf, bbox_data, bbox_index, model_label
+                )
+                regions.append(region)
+
+            for point_index, xyn in enumerate(point_xyn):
+                point_conf = keypoints_data.conf[bbox_index][point_index]
+                if point_conf < self.point_threshold:
+                    continue
+
+                x, y = xyn.tolist()
+                index_name = f"{model_label}::{point_index}"
+                if index_name not in self.point_map:
+                    logger.warning(
+                        f"Point {index_name} not found in point map, "
+                        f"you have to define it in the labeling config, e.g.:\n"
+                        f'<Label value="nose" predicted_values="person" index="1" />'
+                    )
+                    continue
+                point_label = self.point_map[index_name]
+
+                # Add new region with keypoint
+                region = {
+                    "from_name": self.from_name,
+                    "to_name": self.to_name,
+                    "type": "keypointlabels",
+                    "value": {
+                        "keypointlabels": [point_label],  # Keypoint label
+                        "width": self.point_size
+                        / image_width
+                        * 100,  # Keypoint width, just visual styling
+                        "x": x,
+                        "y": y,
+                    },
+                    "meta": {
+                        "text": [f"bbox-{bbox_index}"]  # Group keypoints by bbox index
+                    },
+                    "score": float(point_conf),
+                }
+                # If bboxes are used, group keypoints by bbox
+                if self.add_bboxes:
+                    region["parentID"] = f"bbox-{bbox_index}"
+                regions.append(region)
+        return regions
+
+    def create_bounding_box(self, bbox_conf, bbox_data, bbox_index, model_label):
+        # Add parent bbox that contains all keypoints
+        x, y, w, h = bbox_data.xywhn[bbox_index].tolist()
+        region = {
+            "id": f"bbox-{bbox_index}",
+            "from_name": self.from_name + "_bbox",
+            "to_name": self.to_name,
+            "type": "rectanglelabels",
+            "value": {
+                "rectanglelabels": [model_label],
+                "x": (x - w / 2) * 100,
+                "y": (y - h / 2) * 100,
+                "width": w * 100,
+                "height": h * 100,
+            },
+            "meta": {"text": [f"bbox-{bbox_index}"]},  # Group keypoints by bbox index
+            "score": float(bbox_conf),
+            "hidden": True,
+        }
+        return region
+
+
+# Pre-load and cache default model at startup
+KeypointLabelsModel.get_cached_model(KeypointLabelsModel.model_path)
diff --git a/label_studio_ml/examples/yolo/control_models/polygon_labels.py b/label_studio_ml/examples/yolo/control_models/polygon_labels.py
new file mode 100644
index 000000000..f271ac16f
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/polygon_labels.py
@@ -0,0 +1,76 @@
+import logging
+
+from control_models.base import ControlModel
+from typing import List, Dict
+
+
+logger = logging.getLogger(__name__)
+
+
+class PolygonLabelsModel(ControlModel):
+    """
+    Class representing a PolygonLabels control tag for YOLO model.
+    """
+
+    type = "PolygonLabels"
+    model_path = "yolov8n-seg.pt"
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        # check object tag type
+        if control.objects[0].tag != "Image":
+            return False
+        return control.tag == cls.type
+
+    def predict_regions(self, path) -> List[Dict]:
+        results = self.model.predict(path)
+        return self.create_polygons(results, path)
+
+    def create_polygons(self, results, path):
+        logger.debug(f"create_polygons: {self.from_name}")
+        data = results[0].masks  # take masks from the first frame
+        regions = []
+
+        for i in range(len(data)):
+            score = float(results[0].boxes.conf[i])  # tensor => float
+            points = (
+                data.xyn[i] * 100
+            )  # get the polygon points for the current instance
+            model_label = self.model.names[int(results[0].boxes.cls[i])]
+
+            logger.debug(
+                "----------------------\n"
+                f"task id > {path}\n"
+                f"type: {self.control}\n"
+                f"polygon points > {points}\n"
+                f"model label > {model_label}\n"
+                f"score > {score}\n"
+            )
+
+            # bbox score is too low
+            if score < self.score_threshold:
+                continue
+
+            # there is no mapping between model label and LS label
+            if model_label not in self.label_map:
+                continue
+            output_label = self.label_map[model_label]
+
+            # add new region with polygon
+            region = {
+                "from_name": self.from_name,
+                "to_name": self.to_name,
+                "type": "polygonlabels",
+                "value": {
+                    "polygonlabels": [output_label],
+                    "points": points.tolist(),  # Converting the tensor to a list for JSON serialization
+                    "closed": True,
+                },
+                "score": score,
+            }
+            regions.append(region)
+        return regions
+
+
+# pre-load and cache default model at startup
+PolygonLabelsModel.get_cached_model(PolygonLabelsModel.model_path)
diff --git a/label_studio_ml/examples/yolo/control_models/rectangle_labels.py b/label_studio_ml/examples/yolo/control_models/rectangle_labels.py
new file mode 100644
index 000000000..ec7177ccf
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/rectangle_labels.py
@@ -0,0 +1,98 @@
+import logging
+
+from control_models.base import ControlModel
+from typing import List, Dict
+from label_studio_sdk.label_interface.control_tags import ControlTag
+
+
+logger = logging.getLogger(__name__)
+
+
+def is_obb(control: ControlTag) -> bool:
+    """Check if the model should use oriented bounding boxes (OBB)
+    based on the control tag attribute `model_obb` from the labeling config.
+    """
+    return control.attr.get("model_obb", "false").lower() in ["true", "yes", "1"]
+
+
+class RectangleLabelsModel(ControlModel):
+    """
+    Class representing a RectangleLabels (bounding boxes) control tag for YOLO model.
+    """
+
+    type = "RectangleLabels"
+    model_path = "yolov8m.pt"
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        # check object tag type
+        if control.objects[0].tag != "Image":
+            return False
+        if is_obb(control):
+            return False
+        return control.tag == cls.type
+
+    def predict_regions(self, path) -> List[Dict]:
+        results = self.model.predict(path)
+        self.debug_plot(results[0].plot())
+
+        # oriented bounding boxes are detected, but it should be processed by RectangleLabelsObbModel
+        if results[0].obb is not None and results[0].boxes is None:
+            raise ValueError(
+                "Oriented bounding boxes are detected in the YOLO model results. "
+                'However, `model_obb="true"` is not set at the RectangleLabels tag '
+                "in the labeling config."
+            )
+
+        # simple bounding boxes without rotation
+        return self.create_rectangles(results, path)
+
+    def create_rectangles(self, results, path):
+        """Simple bounding boxes without rotation"""
+        logger.debug(f"create_rectangles: {self.from_name}")
+        data = results[0].boxes  # take bboxes from the first frame
+        regions = []
+
+        for i in range(data.shape[0]):  # iterate over items
+            score = float(data.conf[i])  # tensor => float
+            x, y, w, h = data.xywhn[i].tolist()
+            model_label = self.model.names[int(data.cls[i])]
+
+            logger.debug(
+                "----------------------\n"
+                f"task id > {path}\n"
+                f"type: {self.control}\n"
+                f"x, y, w, h > {x, y, w, h}\n"
+                f"model label > {model_label}\n"
+                f"score > {score}\n"
+            )
+
+            # bbox score is too low
+            if score < self.score_threshold:
+                continue
+
+            # there is no mapping between model label and LS label
+            if model_label not in self.label_map:
+                continue
+            output_label = self.label_map[model_label]
+
+            # add new region with rectangle
+            region = {
+                "from_name": self.from_name,
+                "to_name": self.to_name,
+                "type": "rectanglelabels",
+                "value": {
+                    "rectanglelabels": [output_label],
+                    "x": (x - w / 2) * 100,
+                    "y": (y - h / 2) * 100,
+                    "width": w * 100,
+                    "height": h * 100,
+                },
+                "score": score,
+            }
+            regions.append(region)
+        return regions
+
+
+# pre-load and cache default model at startup
+RectangleLabelsModel.get_cached_model(RectangleLabelsModel.model_path)
diff --git a/label_studio_ml/examples/yolo/control_models/rectangle_labels_obb.py b/label_studio_ml/examples/yolo/control_models/rectangle_labels_obb.py
new file mode 100644
index 000000000..44b9b891e
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/rectangle_labels_obb.py
@@ -0,0 +1,92 @@
+import logging
+
+from control_models.base import ControlModel
+from control_models.rectangle_labels import is_obb
+from typing import List, Dict
+from label_studio_sdk.converter.utils import convert_yolo_obb_to_annotation
+
+
+logger = logging.getLogger(__name__)
+
+
+class RectangleLabelsObbModel(ControlModel):
+    """
+    Class representing a RectangleLabels OBB
+    (oriented bounding boxes, rotated bounding boxes)
+    control tag for YOLO model.
+    """
+
+    type = "RectangleLabels"
+    model_path = "yolov8n-obb.pt"
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        # check object tag type
+        if control.objects[0].tag != "Image":
+            return False
+        if not is_obb(control):
+            return False
+        return control.tag == cls.type
+
+    def predict_regions(self, path) -> List[Dict]:
+        results = self.model.predict(path)
+        self.debug_plot(results[0].plot())
+
+        # simple bounding boxes without rotation
+        if results[0].obb is None:
+            raise ValueError(
+                "Simple bounding boxes are detected in the YOLO model results. "
+                'However, `model_obb="true"` is set at the RectangleLabels tag '
+                "in the labeling config. Set it to `false` to use simple bounding boxes."
+            )
+
+        # oriented bounding boxes with rotation (yolo obb model)
+        return self.create_rotated_rectangles(results, path)
+
+    def create_rotated_rectangles(self, results, path):
+        """YOLO OBB: oriented bounding boxes"""
+        logger.debug(f"create_rotated_rectangles: {self.from_name}")
+        data = results[0].obb  # take bboxes from the first frame
+        regions = []
+
+        for i in range(data.shape[0]):  # iterate over items
+            score = float(data.conf[i])  # tensor => float
+            model_label = self.model.names[int(data.cls[i])]
+            original_height, original_width = data.orig_shape
+            value = convert_yolo_obb_to_annotation(
+                data.xyxyxyxy[i].tolist(), original_width, original_height
+            )
+
+            logger.debug(
+                "----------------------\n"
+                f"task id > {path}\n"
+                f"type: {self.control}\n"
+                f"x, y, w, h, r > {value}\n"
+                f"model label > {model_label}\n"
+                f"score > {score}\n"
+            )
+
+            # bbox score is too low
+            if score < self.score_threshold:
+                continue
+
+            # there is no mapping between model label and LS label
+            if model_label not in self.label_map:
+                continue
+            output_label = self.label_map[model_label]
+            value["rectanglelabels"] = [output_label]
+
+            # add new region with rectangle
+            region = {
+                "from_name": self.from_name,
+                "to_name": self.to_name,
+                "type": "rectanglelabels",
+                "value": value,
+                "score": score,
+            }
+            regions.append(region)
+        return regions
+
+
+# pre-load and cache default model at startup
+RectangleLabelsObbModel.get_cached_model(RectangleLabelsObbModel.model_path)
diff --git a/label_studio_ml/examples/yolo/control_models/timeline_labels.py b/label_studio_ml/examples/yolo/control_models/timeline_labels.py
new file mode 100644
index 000000000..eacdbe23c
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/timeline_labels.py
@@ -0,0 +1,97 @@
+import logging
+from control_models.base import ControlModel
+from typing import List, Dict
+
+logger = logging.getLogger(__name__)
+
+
+class TimelineLabelsModel(ControlModel):
+    """
+    Class representing a TimelineLabels control tag for YOLO model.
+    """
+
+    type = "TimelineLabels"
+    model_path = "yolov8n-cls.pt"
+
+    @classmethod
+    def is_control_matched(cls, control) -> bool:
+        # Check object tag type
+        if control.objects[0].tag != "Video":
+            return False
+        # Support TimelineLabels
+        return control.tag == cls.type
+
+    def predict_regions(self, video_path) -> List[Dict]:
+        # Assuming that `self.model.predict` can take a video file path and output predictions for each frame
+        frame_results = self.model.predict(video_path)
+        self.debug_plot(frame_results[0].plot())
+
+        return self.create_timelines(frame_results, video_path)
+
+    def create_timelines(self, frame_results, video_path):
+        logger.debug(f"create_timelines: {self.from_name}")
+
+        # Initialize dictionary to keep track of ongoing segments for each label
+        ongoing_segments = {label: None for label in self.model.names}
+        timeline_labels = []
+
+        for i, frame_result in enumerate(frame_results):
+            # Get probabilities for all labels for the current frame
+            probs = frame_result.probs.numpy().data
+
+            for label_index, prob in enumerate(probs):
+                label = self.model.names[label_index]
+
+                if prob >= self.score_threshold:
+                    # Start or continue a segment for this label
+                    if ongoing_segments[label] is None:
+                        # Start a new segment
+                        ongoing_segments[label] = {"start": i}
+                else:
+                    # If a segment was ongoing, close it
+                    if ongoing_segments[label] is not None:
+                        timeline_labels.append(
+                            {
+                                "id": f"{video_path}_{label}_{ongoing_segments[label]['start']}_{i}",
+                                "type": "timelinelabels",
+                                "value": {
+                                    "ranges": [
+                                        {
+                                            "start": ongoing_segments[label]["start"],
+                                            "end": i,
+                                        }
+                                    ],
+                                    "timelinelabels": [label],
+                                },
+                                "origin": "manual",
+                                "to_name": self.to_name,
+                                "from_name": self.from_name,
+                            }
+                        )
+                        # Reset the segment for this label
+                        ongoing_segments[label] = None
+
+        # Close any ongoing segments at the end of the video
+        for label, segment in ongoing_segments.items():
+            if segment is not None:
+                timeline_labels.append(
+                    {
+                        "id": f"{video_path}_{label}_{segment['start']}_{len(frame_results)}",
+                        "type": "timelinelabels",
+                        "value": {
+                            "ranges": [
+                                {"start": segment["start"], "end": len(frame_results)}
+                            ],
+                            "timelinelabels": [label],
+                        },
+                        "origin": "manual",
+                        "to_name": self.to_name,
+                        "from_name": self.from_name,
+                    }
+                )
+
+        return timeline_labels
+
+
+# Pre-load and cache the default model at startup
+TimelineLabelsModel.get_cached_model(TimelineLabelsModel.model_path)
diff --git a/label_studio_ml/examples/yolo/control_models/video_rectangle.py b/label_studio_ml/examples/yolo/control_models/video_rectangle.py
new file mode 100644
index 000000000..ed3158944
--- /dev/null
+++ b/label_studio_ml/examples/yolo/control_models/video_rectangle.py
@@ -0,0 +1,222 @@
+import os
+import cv2
+import logging
+import yaml
+import hashlib
+
+from collections import defaultdict
+from control_models.base import ControlModel, MODEL_ROOT
+from label_studio_sdk.label_interface.control_tags import ControlTag
+from typing import List, Dict, Union
+
+
+logger = logging.getLogger(__name__)
+
+
+class VideoRectangleModel(ControlModel):
+    """
+    Class representing a RectangleLabels (bounding boxes) control tag for YOLO model.
+    """
+
+    type = "VideoRectangle"
+    model_path = "yolov8n.pt"
+
+    @classmethod
+    def is_control_matched(cls, control: ControlTag) -> bool:
+        # check object tag type
+        if control.objects[0].tag != "Video":
+            return False
+        # check control type VideoRectangle
+        return control.tag == cls.type
+
+    @staticmethod
+    def get_from_name_for_label_map(label_interface, target_name) -> str:
+        """VideoRectangle doesn't have labels inside, and we should find a connected Labels tag
+        and return its name as a source for the label map.
+        """
+        target: ControlTag = label_interface.get_control(target_name)
+        if not target:
+            raise ValueError(f'Control tag with name "{target_name}" not found')
+
+        for connected in label_interface.controls:
+            if connected.tag == "Labels" and connected.to_name == target.to_name:
+                return connected.name
+
+        logger.error("VideoRectangle detected, but no connected 'Labels' tag found")
+
+    @staticmethod
+    def get_video_duration(path):
+        if not os.path.exists(path):
+            raise ValueError(f"Video file not found: {path}")
+        video = cv2.VideoCapture(path)
+        fps = video.get(cv2.CAP_PROP_FPS)
+        frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+        duration = frame_count / fps
+        logger.info(
+            f"Video duration: {duration} seconds, {frame_count} frames, {fps} fps"
+        )
+        return duration
+
+    def predict_regions(self, path) -> List[Dict]:
+        # bounding box parameters
+        # https://docs.ultralytics.com/modes/track/?h=track#tracking-arguments
+        conf = float(self.control.attr.get("model_conf", 0.25))
+        iou = float(self.control.attr.get("model_iou", 0.70))
+
+        # tracking parameters
+        # https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers
+        tracker_name = self.control.attr.get(
+            "model_tracker", "botsort"
+        )  # or 'bytetrack'
+        original = f"{MODEL_ROOT}/{tracker_name}.yaml"
+        tmp_yaml = self.update_tracker_params(original, prefix=tracker_name + "_")
+        tracker = tmp_yaml if tmp_yaml else original
+
+        # run model track
+        try:
+            results = self.model.track(path, conf=conf, iou=iou, tracker=tracker)
+        finally:
+            # clean temporary file
+            if tmp_yaml and os.path.exists(tmp_yaml):
+                os.remove(tmp_yaml)
+
+        # convert model results to label studio regions
+        return self.create_video_rectangles(results, path)
+
+    def create_video_rectangles(self, results, path):
+        """Create regions of video rectangles from the yolo tracker results"""
+        frames_count, duration = len(results), self.get_video_duration(path)
+        logger.debug(
+            f"create_video_rectangles: {self.from_name}, {frames_count} frames"
+        )
+
+        tracks = defaultdict(list)
+        track_labels = dict()
+        for frame, result in enumerate(results):
+            data = result.boxes
+            if not data.is_track:
+                continue
+
+            for i, track_id in enumerate(data.id.tolist()):
+                score = float(data.conf[i])
+                x, y, w, h = data.xywhn[i].tolist()
+                # get label
+                model_label = self.model.names[int(data.cls[i])]
+                if model_label not in self.label_map:
+                    continue
+                output_label = self.label_map[model_label]
+                track_labels[track_id] = output_label
+
+                box = {
+                    "frame": frame + 1,
+                    "enabled": True,
+                    "rotation": 0,
+                    "x": (x - w / 2) * 100,
+                    "y": (y - h / 2) * 100,
+                    "width": w * 100,
+                    "height": h * 100,
+                    "time": (frame + 1) * (duration / frames_count),
+                    "score": score,
+                }
+                tracks[track_id].append(box)
+
+        regions = []
+        for track_id in tracks:
+            sequence = tracks[track_id]
+            sequence = self.process_lifespans_enabled(sequence)
+
+            label = track_labels[track_id]
+            region = {
+                "from_name": self.from_name,
+                "to_name": self.to_name,
+                "type": "videorectangle",
+                "value": {
+                    "framesCount": frames_count,
+                    "duration": duration,
+                    "sequence": sequence,
+                    "labels": [label],
+                },
+                "score": max([frame_info["score"] for frame_info in sequence]),
+                "origin": "manual",
+            }
+            regions.append(region)
+
+        return regions
+
+    @staticmethod
+    def process_lifespans_enabled(sequence: List[Dict]) -> List[Dict]:
+        """This function detects gaps in the sequence of bboxes
+        and disables lifespan line for the gaps assigning "enabled": False
+        to the last bboxes in the whole span sequence.
+        """
+        prev = None
+        for i, box in enumerate(sequence):
+            if prev is None:
+                prev = sequence[i]
+                continue
+            if box["frame"] - prev["frame"] > 1:
+                sequence[i - 1]["enabled"] = False
+            prev = sequence[i]
+
+        # the last frame enabled is false to turn off lifespan line
+        sequence[-1]["enabled"] = False
+        return sequence
+
+    @staticmethod
+    def generate_hash_filename(extension=".yaml"):
+        """Store yaml configs as temporary files just for one model.track() run"""
+        hash_name = hashlib.sha256(os.urandom(16)).hexdigest()
+        os.makedirs(f"{MODEL_ROOT}/tmp/", exist_ok=True)
+        return f"{MODEL_ROOT}/tmp/{hash_name}{extension}"
+
+    def update_tracker_params(self, yaml_path: str, prefix: str) -> Union[str, None]:
+        """Update tracker parameters in the yaml file with the attributes from the ControlTag,
+        e.g. <VideoRectangle model_tracker="bytetrack" bytetrack_max_age="10" bytetrack_min_hits="3" />
+        or <VideoRectangle model_tracker="botsort" botsort_max_age="10" botsort_min_hits="3" />
+        Args:
+            yaml_path: Path to the original yaml file.
+            prefix: Prefix for attributes of control tag to extract
+        Returns:
+            The file path for new yaml with updated parameters
+        """
+        # check if there are any custom parameters in the labeling config
+        for attr_name, attr_value in self.control.attr.items():
+            if attr_name.startswith(prefix):
+                break
+        else:
+            # no custom parameters, exit
+            return None
+
+        # Load the original yaml file
+        with open(yaml_path, "r") as file:
+            config = yaml.safe_load(file)
+
+        # Extract parameters with prefix from ControlTag
+        for attr_name, attr_value in self.control.attr.items():
+            if attr_name.startswith(prefix):
+                # Remove prefix and update the corresponding yaml key
+                key = attr_name[len(prefix) :]
+
+                # Convert value to the appropriate type (bool, int, float, etc.)
+                if isinstance(config[key], bool):
+                    attr_value = attr_value.lower() == "true"
+                elif isinstance(config[key], int):
+                    attr_value = int(attr_value)
+                elif isinstance(config[key], float):
+                    attr_value = float(attr_value)
+
+                config[key] = attr_value
+
+        # Generate a new filename with a random hash
+        new_yaml_filename = self.generate_hash_filename()
+
+        # Save the updated config to a new yaml file
+        with open(new_yaml_filename, "w") as file:
+            yaml.dump(config, file)
+
+        # Return the new filename
+        return new_yaml_filename
+
+
+# pre-load and cache default model at startup
+VideoRectangleModel.get_cached_model(VideoRectangleModel.model_path)
diff --git a/label_studio_ml/examples/yolo/docker-compose.yml b/label_studio_ml/examples/yolo/docker-compose.yml
new file mode 100644
index 000000000..bbdbd24fd
--- /dev/null
+++ b/label_studio_ml/examples/yolo/docker-compose.yml
@@ -0,0 +1,48 @@
+version: "3.8"
+
+services:
+  yolo:
+    container_name: yolo
+    image: humansignal/yolo:v0
+    build:
+      context: .
+      args:
+        TEST_ENV: ${TEST_ENV}
+    environment:
+      # specify these parameters if you want to use basic auth for the model server
+      - BASIC_AUTH_USER=
+      - BASIC_AUTH_PASS=
+      # set the log level for the model server
+      - LOG_LEVEL=DEBUG
+      # any other parameters that you want to pass to the model server
+      - ANY=PARAMETER
+      # specify the number of workers and threads for the model server
+      - WORKERS=1
+      - THREADS=8
+      # specify the model directory (likely you don't need to change this)
+      - MODEL_DIR=/data/models
+      - PYTHONPATH=/app
+
+      # Specify the Label Studio URL and API key to access
+      # uploaded, local storage and cloud storage files.
+      # Do not use 'localhost' as it does not work within Docker containers.
+      # Use prefix 'http://' or 'https://' for the URL always.
+      # Determine the actual IP using 'ifconfig' (Linux/Mac) or 'ipconfig' (Windows).
+      - LABEL_STUDIO_URL=
+      - LABEL_STUDIO_API_KEY=
+
+      # YOLO parameters
+
+      # Allow to use custom `model_path` in labeling configurations
+      - ALLOW_CUSTOM_MODEL_PATH=true
+      # Show matplotlib debug plot for YOLO predictions
+      - DEBUG_PLOT=false
+      # Default score threshold, which is used to filter out low-confidence predictions,
+      # you can change it in the labeling configuration using `model_score_threshold` parameter in the control tags
+      - MODEL_SCORE_THRESHOLD=0.5
+      # Model root directory, where the YOLO model files are stored
+      - MODEL_ROOT=/app/models
+    ports:
+      - "9090:9090"
+    volumes:
+      - "./data/server:/data"
diff --git a/label_studio_ml/examples/yolo/model.py b/label_studio_ml/examples/yolo/model.py
new file mode 100644
index 000000000..1fc702f47
--- /dev/null
+++ b/label_studio_ml/examples/yolo/model.py
@@ -0,0 +1,176 @@
+import os
+import logging
+
+from label_studio_ml.model import LabelStudioMLBase
+from label_studio_ml.response import ModelResponse
+from label_studio_ml.utils import DATA_UNDEFINED_NAME
+from label_studio_sdk._extensions.label_studio_tools.core.utils.io import get_local_path
+
+from control_models.base import ControlModel
+from control_models.choices import ChoicesModel
+from control_models.rectangle_labels import RectangleLabelsModel
+from control_models.rectangle_labels_obb import RectangleLabelsObbModel
+from control_models.polygon_labels import PolygonLabelsModel
+from control_models.keypoint_labels import KeypointLabelsModel
+from control_models.video_rectangle import VideoRectangleModel
+# from control_models.timeline_labels import TimelineLabelsModel  # Not yet implemented completely
+from typing import List, Dict, Optional
+
+
+logger = logging.getLogger(__name__)
+if not os.getenv("LOG_LEVEL"):
+    logger.setLevel(logging.INFO)
+
+# Register available model classes
+available_model_classes = [
+    ChoicesModel,
+    RectangleLabelsModel,
+    RectangleLabelsObbModel,
+    PolygonLabelsModel,
+    KeypointLabelsModel,
+    VideoRectangleModel,
+    # TimelineLabelsModel, # Not yet implemented completely
+]
+
+
+class YOLO(LabelStudioMLBase):
+    """Label Studio ML Backend based on Ultralytics YOLO"""
+
+    def setup(self):
+        """Configure any parameters of your model here"""
+        self.set("model_version", "yolo")
+
+    def detect_control_models(self) -> List[ControlModel]:
+        """Detect control models based on the labeling config.
+        Control models are used to predict regions for different control tags in the labeling config.
+        """
+        control_models = []
+
+        for control in self.label_interface.controls:
+            # skipping tags without toName
+            if not control.to_name:
+                logger.warning(
+                    f'{control.tag} {control.name} has no "toName" attribute, skipping it'
+                )
+                continue
+
+            # match control tag with available control models
+            for model_class in available_model_classes:
+                if model_class.is_control_matched(control):
+                    instance = model_class.create(self, control)
+                    if not instance:
+                        logger.debug(
+                            f"No instance created for {control.tag} {control.name}"
+                        )
+                        continue
+                    if not instance.label_map:
+                        logger.error(
+                            f"No label map built for the '{control.tag}' control tag '{instance.from_name}'.\n"
+                            f"This indicates that your Label Studio config labels do not match the model's labels.\n"
+                            f"To fix this, ensure that the 'value' or 'predicted_values' attribute "
+                            f"in your Label Studio config matches one or more of these model labels.\n"
+                            f"If you don't want to use this control tag for predictions, "
+                            f'add `model_skip="true"` to it.\n'
+                            f"Examples:\n"
+                            f'  <Label value="Car"/>\n'
+                            f'  <Label value="YourLabel" predicted_values="label1,label2"/>\n'
+                            f"Labels provided in your labeling config:\n"
+                            f"  {str(control.labels_attrs)}\n"
+                            f"Available '{instance.model_path}' model labels:\n"
+                            f"  {list(instance.model.names.values())}"
+                        )
+                        continue
+
+                    control_models.append(instance)
+                    logger.debug(f"Control tag with model detected: {instance}")
+                    break
+
+        if not control_models:
+            control_tags = ", ".join([c.type for c in available_model_classes])
+            raise ValueError(
+                f"No suitable control tags (e.g. {control_tags} connected to Image or Video object tags) "
+                f"detected in the label config"
+            )
+
+        return control_models
+
+    def predict(
+        self, tasks: List[Dict], context: Optional[Dict] = None, **kwargs
+    ) -> ModelResponse:
+        """Run YOLO predictions on the tasks
+        :param tasks: [Label Studio tasks in JSON format](https://labelstud.io/guide/task_format.html)
+        :param context: [Label Studio context in JSON format](https://labelstud.io/guide/ml_create)
+        :return model_response
+            ModelResponse(predictions=predictions) with
+            predictions [Predictions array in JSON format]
+            (https://labelstud.io/guide/export.html#Label-Studio-JSON-format-of-annotated-tasks)
+        """
+        logger.info(
+            f"Run prediction on {len(tasks)} tasks, project ID = {self.project_id}"
+        )
+        control_models = self.detect_control_models()
+
+        predictions = []
+        for task in tasks:
+
+            regions = []
+            for model in control_models:
+                task_path = task["data"].get(model.value) or task["data"].get(
+                    DATA_UNDEFINED_NAME
+                )
+                if task_path is None:
+                    raise ValueError(
+                        f"Can't load path using key '{model.value}' from task {task}"
+                    )
+                if not isinstance(task_path, str):
+                    raise ValueError(f"Path should be a string, but got {task_path}")
+
+                # try path as local file or try to load it from Label Studio instance/download via http
+                path = (
+                    task_path
+                    if os.path.exists(task_path)
+                    else get_local_path(task_path, task_id=task.get("id"))
+                )
+                logger.debug(f"load_image: {task_path} => {path}")
+
+                regions += model.predict_regions(path)
+
+            # calculate final score
+            all_scores = [region["score"] for region in regions if "score" in region]
+            avg_score = sum(all_scores) / max(len(all_scores), 1)
+
+            # compose final prediction
+            prediction = {
+                "result": regions,
+                "score": avg_score,
+                "model_version": self.model_version,
+            }
+            predictions.append(prediction)
+
+        return ModelResponse(predictions=predictions)
+
+    def fit(self, event, data, **kwargs):
+        """
+        This method is called each time an annotation is created or updated
+        You can run your logic here to update the model and persist it to the cache
+        It is not recommended to perform long-running operations here, as it will block the main thread
+        Instead, consider running a separate process or a thread (like RQ worker) to perform the training
+        :param event: event type can be ('ANNOTATION_CREATED', 'ANNOTATION_UPDATED', 'START_TRAINING')
+        :param data: the payload received from the event
+        (check [Webhook event reference](https://labelstud.io/guide/webhook_reference.html))
+
+        # use cache to retrieve the data from the previous fit() runs
+        old_data = self.get('my_data')
+        old_model_version = self.get('model_version')
+        print(f'Old data: {old_data}')
+        print(f'Old model version: {old_model_version}')
+
+        # store new data to the cache
+        self.set('my_data', 'my_new_data_value')
+        self.set('model_version', 'my_new_model_version')
+        print(f'New data: {self.get("my_data")}')
+        print(f'New model version: {self.get("model_version")}')
+
+        print('fit() is not implemented!')
+        """
+        raise NotImplementedError("Training is not implemented yet")
diff --git a/label_studio_ml/examples/yolo/models/README.md b/label_studio_ml/examples/yolo/models/README.md
new file mode 100644
index 000000000..fe9d093d1
--- /dev/null
+++ b/label_studio_ml/examples/yolo/models/README.md
@@ -0,0 +1 @@
+Put your YOLO models here.
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/models/botsort.yaml b/label_studio_ml/examples/yolo/models/botsort.yaml
new file mode 100644
index 000000000..2ec6e3150
--- /dev/null
+++ b/label_studio_ml/examples/yolo/models/botsort.yaml
@@ -0,0 +1,19 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Default YOLO tracker settings for BoT-SORT tracker https://github.com/NirAharon/BoT-SORT
+# This file is taken from https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers
+
+tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
+track_high_thresh: 0.5 # threshold for the first association
+track_low_thresh: 0.1 # threshold for the second association
+new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
+track_buffer: 30 # buffer to calculate the time when to remove tracks
+match_thresh: 0.8 # threshold for matching tracks
+fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
+# min_box_area: 10  # threshold for min box areas(for tracker evaluation, not used for now)
+
+# BoT-SORT settings
+gmc_method: sparseOptFlow # method of global motion compensation
+# ReID model related thresh (not supported yet)
+proximity_thresh: 0.5
+appearance_thresh: 0.25
+with_reid: False
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/models/bytetrack.yaml b/label_studio_ml/examples/yolo/models/bytetrack.yaml
new file mode 100644
index 000000000..3bed2182a
--- /dev/null
+++ b/label_studio_ml/examples/yolo/models/bytetrack.yaml
@@ -0,0 +1,12 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Default YOLO tracker settings for ByteTrack tracker https://github.com/ifzhang/ByteTrack
+# This file is taken from https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers
+
+tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
+track_high_thresh: 0.5 # threshold for the first association
+track_low_thresh: 0.1 # threshold for the second association
+new_track_thresh: 0.6 # threshold for init new track if the detection does not match any tracks
+track_buffer: 30 # buffer to calculate the time when to remove tracks
+match_thresh: 0.8 # threshold for matching tracks
+fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
+# min_box_area: 10  # threshold for min box areas(for tracker evaluation, not used for now)
diff --git a/label_studio_ml/examples/yolo/requirements-base.txt b/label_studio_ml/examples/yolo/requirements-base.txt
new file mode 100644
index 000000000..dd671cfd0
--- /dev/null
+++ b/label_studio_ml/examples/yolo/requirements-base.txt
@@ -0,0 +1,3 @@
+gunicorn==22.0.0
+label-studio-ml @ git+https://github.com/HumanSignal/label-studio-ml-backend.git@fix/rnd-117
+label-studio-sdk @ git+https://github.com/HumanSignal/label-studio-sdk.git
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/requirements-test.txt b/label_studio_ml/examples/yolo/requirements-test.txt
new file mode 100644
index 000000000..e8ac9d093
--- /dev/null
+++ b/label_studio_ml/examples/yolo/requirements-test.txt
@@ -0,0 +1,3 @@
+pytest
+pytest-cov
+pytest-mock
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/requirements.txt b/label_studio_ml/examples/yolo/requirements.txt
new file mode 100644
index 000000000..8008433ac
--- /dev/null
+++ b/label_studio_ml/examples/yolo/requirements.txt
@@ -0,0 +1,2 @@
+ultralytics~=8.2.76
+tqdm
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/start.sh b/label_studio_ml/examples/yolo/start.sh
new file mode 100755
index 000000000..449c16e31
--- /dev/null
+++ b/label_studio_ml/examples/yolo/start.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+# Execute the gunicorn command
+exec gunicorn --bind :${PORT:-9090} --workers ${WORKERS:-1} --threads ${THREADS:-4} --timeout 0 _wsgi:app
diff --git a/label_studio_ml/examples/yolo/tests/__init__.py b/label_studio_ml/examples/yolo/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/label_studio_ml/examples/yolo/tests/car.jpg b/label_studio_ml/examples/yolo/tests/car.jpg
new file mode 100644
index 000000000..5b1700648
Binary files /dev/null and b/label_studio_ml/examples/yolo/tests/car.jpg differ
diff --git a/label_studio_ml/examples/yolo/tests/opossum_snow_short.mp4 b/label_studio_ml/examples/yolo/tests/opossum_snow_short.mp4
new file mode 100644
index 000000000..d5296f7b2
Binary files /dev/null and b/label_studio_ml/examples/yolo/tests/opossum_snow_short.mp4 differ
diff --git a/label_studio_ml/examples/yolo/tests/opossum_snow_short.pickle b/label_studio_ml/examples/yolo/tests/opossum_snow_short.pickle
new file mode 100644
index 000000000..27cf8ceb4
Binary files /dev/null and b/label_studio_ml/examples/yolo/tests/opossum_snow_short.pickle differ
diff --git a/label_studio_ml/examples/yolo/tests/opossum_snow_short_1.json b/label_studio_ml/examples/yolo/tests/opossum_snow_short_1.json
new file mode 100644
index 000000000..130f1283d
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/opossum_snow_short_1.json
@@ -0,0 +1,1826 @@
+[
+  {
+    "model_version": "yolo",
+    "result": [
+      {
+        "from_name": "box",
+        "origin": "manual",
+        "score": 0.9261998534202576,
+        "to_name": "video",
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+        "value": {
+          "duration": 1.1678333333333333,
+          "framesCount": 35,
+          "labels": [
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+          ],
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diff --git a/label_studio_ml/examples/yolo/tests/opossum_snow_short_2.json b/label_studio_ml/examples/yolo/tests/opossum_snow_short_2.json
new file mode 100644
index 000000000..357509cd8
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/opossum_snow_short_2.json
@@ -0,0 +1,1220 @@
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+            "person"
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+    "score": 0.7652523687907627
+  }
+]
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/tests/test_choices.py b/label_studio_ml/examples/yolo/tests/test_choices.py
new file mode 100644
index 000000000..ffede904c
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_choices.py
@@ -0,0 +1,130 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+
+```bash
+pip install -r requirements-test.txt
+```
+"""
+
+import pytest
+import json
+
+from label_studio_ml.utils import compare_nested_structures
+from .test_common import client
+
+
+label_configs = [
+    # test 1: one control tag with single choice
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <Choices name="label" toName="image" score_threshold="0.53">
+        <Choice value="Airplane" background="green"/>
+        <Choice value="Car" background="blue" predicted_values="racer, cab"/>
+      </Choices>
+    </View>
+    """,
+    # test 2: one control tag with multi choices
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <Choices name="label" toName="image" choice="multiple" score_threshold="0.1">
+        <Choice value="Grille" background="green"/>
+        <Choice value="Cab" background="blue" predicted_values="racer, cab"/>
+      </Choices>
+    </View>
+    """,
+    # test 3: no choices
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <Choices name="label" toName="image" choice="multiple" score_threshold="0.9">
+        <Choice value="Grille" background="green"/>
+        <Choice value="Cab" background="blue" predicted_values="racer, cab"/>
+      </Choices>
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1: one control tag with single choice
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+    # test 2: one control tag with multi choices
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+    # test 3: no choices
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+]
+
+expected = [
+    # test 1: one control tag with single choice
+    [
+        {
+            "model_version": "yolo",
+            "result": [
+                {
+                    "from_name": "label",
+                    "score": 0.5582300424575806,
+                    "to_name": "image",
+                    "type": "choices",
+                    "value": {"choices": ["Car"]},
+                }
+            ],
+            "score": 0.5582300424575806,
+        }
+    ],
+    # test 2: one control tag with multi choices
+    [
+        {
+            "model_version": "yolo",
+            "result": [
+                {
+                    "from_name": "label",
+                    "score": 0.4747641831636429,
+                    "to_name": "image",
+                    "type": "choices",
+                    "value": {"choices": ["Cab", "Grille"]},
+                }
+            ],
+            "score": 0.4747641831636429,
+        }
+    ],
+    # test 3: no choices
+    [
+        {
+            "model_version": "yolo",
+            "result": [],
+            "score": 0.0,
+        }
+    ],
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, expect", zip(label_configs, tasks, expected)
+)
+def test_choices_predict(client, label_config, task, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+    response = client.post(
+        "/predict", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect)
diff --git a/label_studio_ml/examples/yolo/tests/test_common.py b/label_studio_ml/examples/yolo/tests/test_common.py
new file mode 100644
index 000000000..a1150fae3
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_common.py
@@ -0,0 +1,191 @@
+"""
+This file contains tests for the API
+"""
+
+import os
+import pickle
+import pytest
+import json
+
+from unittest.mock import MagicMock, patch
+from model import YOLO
+
+
+def load_file(path):
+    # json
+    if path.endswith(".json"):
+        with open(path, "r") as f:
+            return json.load(f)
+    # pickle
+    if path.endswith(".pickle"):
+        with open(path, "rb") as f:
+            return pickle.load(f)
+
+
+@pytest.fixture
+def client():
+    from _wsgi import init_app
+
+    app = init_app(model_class=YOLO)
+    app.config["TESTING"] = True
+    with app.test_client() as client:
+        yield client
+
+
+TEST_DIR = os.path.dirname(__file__)
+
+
+label_configs = [
+    # test 1: wrong key in task data
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <RectangleLabels name="label" toName="image" score_threshold="0.25">
+        <Label value="Airplane" background="green"/>
+        <Label value="Car" background="blue" predicted_values="car, truck"/>
+      </RectangleLabels>
+    </View>
+    """,
+    # test 2: model_skip=true
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <RectangleLabels name="label" toName="image" model_skip="true">
+        <Label value="Airplane" background="green"/>
+        <Label value="Car" background="blue" predicted_values="car, truck"/>
+      </RectangleLabels>
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1: wrong key in task data
+    {"data": {"wrong_key": "https://some/path"}},
+    # test 2: model_skip
+    {"data": {"image": "https://some/path"}},
+]
+
+expected = [
+    # test 1: wrong key in task data
+    "Can't load path using key",
+    # test 2: model skip
+    "No suitable control tags",
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, expect", zip(label_configs, tasks, expected)
+)
+def test_label_configs(client, label_config, task, expect, capsys):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+    response = client.post(
+        "/predict", data=json.dumps(data), content_type="application/json"
+    )
+    assert (
+        response.status_code == 500
+    ), "Error was expected, but another status code found"
+
+    # Capture stdout and stderr
+    captured = capsys.readouterr()
+
+    # Check for specific words in the output
+    assert expect in captured.out, "Text not found in error string"
+
+
+@pytest.fixture
+def mock_logger(mocker):
+    return mocker.patch("model.logger")
+
+
+@pytest.fixture
+def mock_ml_backend():
+    return MagicMock()
+
+
+@pytest.fixture
+def mock_label_interface(mock_ml_backend):
+    mock_label_interface = MagicMock()
+    mock_ml_backend.label_interface = mock_label_interface
+    return mock_label_interface
+
+
+@pytest.fixture
+def mock_model_class():
+    mock_model_class = MagicMock()
+    mock_model_class.type = "RectangleLabels"
+    mock_model_class.is_control_matched = MagicMock(return_value=True)
+    mock_model_class.create = MagicMock()
+    return mock_model_class
+
+
+@pytest.fixture
+def available_model_classes(mock_model_class):
+    return [mock_model_class, MagicMock(), MagicMock()]
+
+
+@pytest.fixture
+def yolo_instance(mock_label_interface):
+    yolo = YOLO()
+    yolo.label_interface = mock_label_interface
+    return yolo
+
+
+def test_control_no_to_name(yolo_instance, mock_label_interface, mock_logger):
+    mock_label_interface.controls = [
+        MagicMock(to_name=None, tag="RectangleLabels", name="test_label")
+    ]
+
+    with pytest.raises(ValueError):
+        yolo_instance.detect_control_models()
+
+    mock_logger.warning.assert_called_once()
+
+
+def test_control_no_label_map(
+    yolo_instance,
+    mock_label_interface,
+    mock_model_class,
+    available_model_classes,
+    mock_logger,
+):
+    mock_instance = MagicMock()
+    mock_instance.label_map = None
+    mock_model_class.create.return_value = mock_instance
+    mock_label_interface.controls = [
+        MagicMock(to_name=["image"], tag="RectangleLabels", name="test_label")
+    ]
+
+    with patch("model.available_model_classes", available_model_classes):
+        result = yolo_instance.detect_control_models()
+
+    # Updated expectation to handle control models that should be skipped if label_map is None
+    assert len(result) == 1
+    mock_logger.error.assert_called_once()
+
+
+def test_control_with_valid_label_map(
+    yolo_instance,
+    mock_label_interface,
+    mock_model_class,
+    available_model_classes,
+    mock_logger,
+):
+    mock_instance = MagicMock()
+    mock_instance.label_map = {"car": "Car"}
+    mock_model_class.create.return_value = mock_instance
+    mock_label_interface.controls = [
+        MagicMock(to_name=["image"], tag="RectangleLabels", name="test_label")
+    ]
+
+    with patch("model.available_model_classes", available_model_classes):
+        result = yolo_instance.detect_control_models()
+
+    assert len(result) == 1
+    assert result[0] == mock_instance
+    mock_logger.debug.assert_called_once()
diff --git a/label_studio_ml/examples/yolo/tests/test_keypoint_labels.py b/label_studio_ml/examples/yolo/tests/test_keypoint_labels.py
new file mode 100644
index 000000000..c7bd8424a
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_keypoint_labels.py
@@ -0,0 +1,134 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+
+```bash
+pip install -r requirements-test.txt
+```
+"""
+
+import pytest
+import json
+
+from label_studio_ml.utils import compare_nested_structures
+from .test_common import client, load_file, TEST_DIR
+
+
+label_configs = [
+    # test 1
+    """
+    <View>
+      <KeyPointLabels name="keypoints" toName="image" 
+        score_threshold="0.85" model_point_threshold="0.95" model_add_bboxes="true" model_point_size="1"  
+      >
+        <Label value="nose" predicted_values="person" model_index="0" background="red" />
+
+        <Label value="left_eye" predicted_values="person" model_index="1" background="yellow" />
+        <Label value="right_eye" predicted_values="person" model_index="2" background="yellow" />
+
+        <Label value="left_ear" predicted_values="person" model_index="3" background="purple" />
+        <Label value="right_ear" predicted_values="person" model_index="4" background="purple" />
+
+        <View>
+        <Label value="left_shoulder" predicted_values="person" model_index="5" background="green" />
+        <Label value="left_elbow" predicted_values="person" model_index="7" background="green" />
+        <Label value="left_wrist" predicted_values="person" model_index="9" background="green" />
+
+        <Label value="right_shoulder" predicted_values="person" model_index="6" background="blue" />
+        <Label value="right_elbow" predicted_values="person" model_index="8" background="blue" />
+        <Label value="right_wrist" predicted_values="person" model_index="10" background="blue" />
+        </View>
+
+        <View>
+        <Label value="left_hip" predicted_values="person" model_index="11" background="brown" />
+        <Label value="left_knee" predicted_values="person" model_index="13" background="brown" />
+        <Label value="left_ankle" predicted_values="person" model_index="15" background="brown" />
+
+        <Label value="right_hip" predicted_values="person" model_index="12" background="orange" />
+        <Label value="right_knee" predicted_values="person" model_index="14" background="orange" />
+        <Label value="right_ankle" predicted_values="person" model_index="16" background="orange" />
+        </View>
+        
+        <Label value="right_ankle" predicted_values="just_to_test_warning" model_index="0" background="orange" />
+      </KeyPointLabels>
+      <Image name="image" value="$image" />
+    </View>
+    """,
+    # test 2: no bboxes
+    """
+    <View>
+      <KeyPointLabels name="keypoints" toName="image" 
+        score_threshold="0.85" model_point_threshold="0.96" model_add_bboxes="false" model_point_size="1"  
+      >
+        <Label value="nose" predicted_values="person" model_index="0" background="red" />
+
+        <Label value="left_eye" predicted_values="person" model_index="1" background="yellow" />
+        <Label value="right_eye" predicted_values="person" model_index="2" background="yellow" />
+
+        <Label value="left_ear" predicted_values="person" model_index="3" background="purple" />
+        <Label value="right_ear" predicted_values="person" model_index="4" background="purple" />
+
+        <View>
+        <Label value="left_shoulder" predicted_values="person" model_index="5" background="green" />
+        <Label value="left_elbow" predicted_values="person" model_index="7" background="green" />
+        <Label value="left_wrist" predicted_values="person" model_index="9" background="green" />
+
+        <Label value="right_shoulder" predicted_values="person" model_index="6" background="blue" />
+        <Label value="right_elbow" predicted_values="person" model_index="8" background="blue" />
+        <Label value="right_wrist" predicted_values="person" model_index="10" background="blue" />
+        </View>
+
+        <View>
+        <Label value="left_hip" predicted_values="person" model_index="11" background="brown" />
+        <Label value="left_knee" predicted_values="person" model_index="13" background="brown" />
+        <Label value="left_ankle" predicted_values="person" model_index="15" background="brown" />
+
+        <Label value="right_hip" predicted_values="person" model_index="12" background="orange" />
+        <Label value="right_knee" predicted_values="person" model_index="14" background="orange" />
+        <Label value="right_ankle" predicted_values="person" model_index="16" background="orange" />
+        </View>
+      </KeyPointLabels>
+      <Image name="image" value="$image" />
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+    # test 2: no bbox
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+]
+
+expected = [
+    # test 1
+    load_file(TEST_DIR + "/test_keypoint_labels_1.json"),
+    # test 2: no bbox
+    load_file(TEST_DIR + "/test_keypoint_labels_2.json"),
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, expect", zip(label_configs, tasks, expected)
+)
+def test_keypoints_predict(client, label_config, task, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+    response = client.post(
+        "/predict", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect)
diff --git a/label_studio_ml/examples/yolo/tests/test_keypoint_labels_1.json b/label_studio_ml/examples/yolo/tests/test_keypoint_labels_1.json
new file mode 100644
index 000000000..c96cc0a6a
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_keypoint_labels_1.json
@@ -0,0 +1,150 @@
+[
+  {
+    "model_version": "yolo",
+    "result": [
+      {
+        "from_name": "keypoints_bbox",
+        "hidden": true,
+        "id": "bbox-0",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.8605291843414307,
+        "to_name": "image",
+        "type": "rectanglelabels",
+        "value": {
+          "height": 39.93055522441864,
+          "rectanglelabels": [
+            "person"
+          ],
+          "width": 10.546875,
+          "x": 89.453125,
+          "y": 6.77083283662796
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "parentID": "bbox-0",
+        "score": 0.9874914884567261,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "left_shoulder"
+          ],
+          "width": 0.13020833333333331,
+          "x": 98.1597900390625,
+          "y": 15.064401626586914
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "parentID": "bbox-0",
+        "score": 0.9673970937728882,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "right_shoulder"
+          ],
+          "width": 0.13020833333333331,
+          "x": 92.978515625,
+          "y": 14.892805099487305
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "parentID": "bbox-0",
+        "score": 0.9881816506385803,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "left_hip"
+          ],
+          "width": 0.13020833333333331,
+          "x": 96.12835693359375,
+          "y": 27.02771759033203
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "parentID": "bbox-0",
+        "score": 0.9819034337997437,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "right_hip"
+          ],
+          "width": 0.13020833333333331,
+          "x": 92.79766082763672,
+          "y": 26.574970245361328
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "parentID": "bbox-0",
+        "score": 0.9782900214195251,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "left_knee"
+          ],
+          "width": 0.13020833333333331,
+          "x": 96.00843048095703,
+          "y": 35.76639175415039
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "parentID": "bbox-0",
+        "score": 0.969311535358429,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "right_knee"
+          ],
+          "width": 0.13020833333333331,
+          "x": 92.45714569091797,
+          "y": 34.812171936035156
+        }
+      }
+    ],
+    "score": 0.9618720582553318
+  }
+]
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/tests/test_keypoint_labels_2.json b/label_studio_ml/examples/yolo/tests/test_keypoint_labels_2.json
new file mode 100644
index 000000000..a87e0538b
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_keypoint_labels_2.json
@@ -0,0 +1,122 @@
+[
+  {
+    "model_version": "yolo",
+    "result": [
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.9874914884567261,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "left_shoulder"
+          ],
+          "width": 0.13020833333333331,
+          "x": 98.1597900390625,
+          "y": 15.064401626586914
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.9673970937728882,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "right_shoulder"
+          ],
+          "width": 0.13020833333333331,
+          "x": 92.978515625,
+          "y": 14.892805099487305
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.9881816506385803,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "left_hip"
+          ],
+          "width": 0.13020833333333331,
+          "x": 96.12835693359375,
+          "y": 27.02771759033203
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.9819034337997437,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "right_hip"
+          ],
+          "width": 0.13020833333333331,
+          "x": 92.79766082763672,
+          "y": 26.574970245361328
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.9782900214195251,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "left_knee"
+          ],
+          "width": 0.13020833333333331,
+          "x": 96.00843048095703,
+          "y": 35.76639175415039
+        }
+      },
+      {
+        "from_name": "keypoints",
+        "meta": {
+          "text": [
+            "bbox-0"
+          ]
+        },
+        "score": 0.969311535358429,
+        "to_name": "image",
+        "type": "keypointlabels",
+        "value": {
+          "keypointlabels": [
+            "right_knee"
+          ],
+          "width": 0.13020833333333331,
+          "x": 92.45714569091797,
+          "y": 34.812171936035156
+        }
+      }
+    ],
+    "score": 0.9787625372409821
+  }
+]
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/tests/test_polygon_labels.json b/label_studio_ml/examples/yolo/tests/test_polygon_labels.json
new file mode 100644
index 000000000..595bd217c
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_polygon_labels.json
@@ -0,0 +1,1746 @@
+[
+  {
+    "model_version": "yolo",
+    "result": [
+      {
+        "from_name": "label",
+        "score": 0.860294759273529,
+        "to_name": "image",
+        "type": "polygonlabels",
+        "value": {
+          "closed": true,
+          "points": [
+            [
+              93.43750762939453,
+              7.083333492279053
+            ],
+            [
+              93.43750762939453,
+              8.333333969116211
+            ],
+            [
+              93.28125,
+              8.541666984558105
+            ],
+            [
+              93.28125,
+              9.583333015441895
+            ],
+            [
+              93.125,
+              9.791666984558105
+            ],
+            [
+              93.125,
+              11.875000953674316
+            ],
+            [
+              92.96875,
+              12.083333015441895
+            ],
+            [
+              92.96875,
+              12.916666030883789
+            ],
+            [
+              92.8125,
+              13.124999046325684
+            ],
+            [
+              92.8125,
+              13.541666984558105
+            ],
+            [
+              92.65625762939453,
+              13.75
+            ],
+            [
+              92.65625762939453,
+              14.375000953674316
+            ],
+            [
+              92.5,
+              14.583333015441895
+            ],
+            [
+              92.5,
+              14.791667938232422
+            ],
+            [
+              92.1875,
+              15.208333969116211
+            ],
+            [
+              92.1875,
+              15.416666984558105
+            ],
+            [
+              92.03125,
+              15.625
+            ],
+            [
+              92.03125,
+              15.833334922790527
+            ],
+            [
+              91.87500762939453,
+              16.04166603088379
+            ],
+            [
+              91.87500762939453,
+              16.45833396911621
+            ],
+            [
+              91.5625,
+              16.875
+            ],
+            [
+              91.5625,
+              17.08333396911621
+            ],
+            [
+              91.40625,
+              17.291667938232422
+            ],
+            [
+              91.40625,
+              17.708332061767578
+            ],
+            [
+              91.25,
+              17.916667938232422
+            ],
+            [
+              91.25,
+              18.54166603088379
+            ],
+            [
+              91.09375762939453,
+              18.75
+            ],
+            [
+              91.09375762939453,
+              19.16666603088379
+            ],
+            [
+              90.9375,
+              19.375
+            ],
+            [
+              90.9375,
+              19.791667938232422
+            ],
+            [
+              90.78125,
+              20.0
+            ],
+            [
+              90.78125,
+              20.625001907348633
+            ],
+            [
+              90.625,
+              20.833332061767578
+            ],
+            [
+              90.625,
+              21.25
+            ],
+            [
+              90.46875,
+              21.45833396911621
+            ],
+            [
+              90.46875,
+              21.66666603088379
+            ],
+            [
+              90.31250762939453,
+              21.875
+            ],
+            [
+              89.6875,
+              21.875
+            ],
+            [
+              89.6875,
+              25.0
+            ],
+            [
+              90.31250762939453,
+              25.0
+            ],
+            [
+              90.46875,
+              25.20833396911621
+            ],
+            [
+              90.46875,
+              25.416669845581055
+            ],
+            [
+              90.78125,
+              25.833332061767578
+            ],
+            [
+              90.78125,
+              26.249998092651367
+            ],
+            [
+              90.9375,
+              26.458335876464844
+            ],
+            [
+              90.9375,
+              28.541669845581055
+            ],
+            [
+              91.09375762939453,
+              28.750001907348633
+            ],
+            [
+              91.09375762939453,
+              30.20833396911621
+            ],
+            [
+              91.25,
+              30.416667938232422
+            ],
+            [
+              91.25,
+              35.833335876464844
+            ],
+            [
+              91.40625,
+              36.04166793823242
+            ],
+            [
+              91.40625,
+              36.66666793823242
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+              36.25
+            ],
+            [
+              19.21875,
+              36.66666793823242
+            ],
+            [
+              19.375,
+              36.875
+            ],
+            [
+              19.375,
+              37.91666793823242
+            ],
+            [
+              19.53125,
+              38.125003814697266
+            ],
+            [
+              19.53125,
+              38.33333206176758
+            ],
+            [
+              19.843751907348633,
+              38.75
+            ],
+            [
+              20.0,
+              38.75
+            ],
+            [
+              20.15625,
+              38.958335876464844
+            ],
+            [
+              20.46875,
+              38.958335876464844
+            ],
+            [
+              20.625001907348633,
+              39.16666793823242
+            ],
+            [
+              21.25,
+              39.16666793823242
+            ],
+            [
+              21.71875,
+              39.79166793823242
+            ],
+            [
+              21.71875,
+              40.20833206176758
+            ],
+            [
+              21.875,
+              40.41666793823242
+            ],
+            [
+              21.875,
+              40.625
+            ],
+            [
+              22.34375,
+              41.250003814697266
+            ],
+            [
+              22.5,
+              41.250003814697266
+            ],
+            [
+              22.65625,
+              41.45833206176758
+            ],
+            [
+              22.968751907348633,
+              41.45833206176758
+            ],
+            [
+              23.125,
+              41.666664123535156
+            ],
+            [
+              23.4375,
+              41.666664123535156
+            ],
+            [
+              23.59375,
+              41.875
+            ],
+            [
+              23.90625,
+              41.875
+            ],
+            [
+              24.0625,
+              42.083335876464844
+            ],
+            [
+              24.6875,
+              42.083335876464844
+            ],
+            [
+              24.84375,
+              41.875
+            ],
+            [
+              25.0,
+              41.875
+            ],
+            [
+              25.312501907348633,
+              41.45833206176758
+            ],
+            [
+              25.312501907348633,
+              41.250003814697266
+            ],
+            [
+              25.468751907348633,
+              41.04166793823242
+            ],
+            [
+              25.468751907348633,
+              40.20833206176758
+            ],
+            [
+              25.312501907348633,
+              40.0
+            ],
+            [
+              25.312501907348633,
+              39.16666793823242
+            ],
+            [
+              25.15625,
+              38.958335876464844
+            ],
+            [
+              25.15625,
+              37.29166793823242
+            ],
+            [
+              25.0,
+              37.08333206176758
+            ],
+            [
+              25.0,
+              33.75
+            ],
+            [
+              25.15625,
+              33.54166793823242
+            ],
+            [
+              25.15625,
+              33.125
+            ],
+            [
+              25.312501907348633,
+              32.91666793823242
+            ],
+            [
+              25.78125,
+              32.91666793823242
+            ],
+            [
+              26.5625,
+              31.875001907348633
+            ],
+            [
+              26.71875,
+              32.08333206176758
+            ],
+            [
+              26.875001907348633,
+              32.08333206176758
+            ],
+            [
+              27.1875,
+              32.5
+            ],
+            [
+              27.1875,
+              33.125
+            ],
+            [
+              27.34375,
+              33.333335876464844
+            ],
+            [
+              27.34375,
+              33.95833206176758
+            ],
+            [
+              27.5,
+              34.16666793823242
+            ],
+            [
+              27.5,
+              34.583335876464844
+            ],
+            [
+              27.656251907348633,
+              34.79166793823242
+            ],
+            [
+              27.656251907348633,
+              35.000003814697266
+            ],
+            [
+              27.812501907348633,
+              35.20833206176758
+            ],
+            [
+              28.28125,
+              35.20833206176758
+            ],
+            [
+              28.28125,
+              29.583335876464844
+            ],
+            [
+              27.656251907348633,
+              29.583335876464844
+            ],
+            [
+              27.5,
+              29.374998092651367
+            ],
+            [
+              27.5,
+              29.16666603088379
+            ],
+            [
+              27.34375,
+              28.958332061767578
+            ],
+            [
+              27.34375,
+              28.541669845581055
+            ],
+            [
+              27.1875,
+              28.33333396911621
+            ],
+            [
+              27.1875,
+              27.08333396911621
+            ],
+            [
+              27.031251907348633,
+              26.875001907348633
+            ],
+            [
+              27.031251907348633,
+              26.458335876464844
+            ],
+            [
+              26.875001907348633,
+              26.249998092651367
+            ],
+            [
+              26.875001907348633,
+              25.625001907348633
+            ],
+            [
+              26.71875,
+              25.416669845581055
+            ],
+            [
+              26.71875,
+              25.0
+            ],
+            [
+              26.5625,
+              24.79166603088379
+            ],
+            [
+              26.5625,
+              24.16666603088379
+            ],
+            [
+              26.40625,
+              23.958332061767578
+            ],
+            [
+              26.40625,
+              23.541667938232422
+            ],
+            [
+              26.250001907348633,
+              23.333335876464844
+            ],
+            [
+              26.250001907348633,
+              22.291667938232422
+            ],
+            [
+              26.093751907348633,
+              22.08333396911621
+            ],
+            [
+              26.093751907348633,
+              20.833332061767578
+            ],
+            [
+              25.9375,
+              20.625001907348633
+            ],
+            [
+              25.9375,
+              19.791667938232422
+            ],
+            [
+              25.78125,
+              19.58333396911621
+            ],
+            [
+              25.78125,
+              19.16666603088379
+            ],
+            [
+              25.625,
+              18.95833396911621
+            ],
+            [
+              25.625,
+              18.125
+            ],
+            [
+              25.468751907348633,
+              17.916667938232422
+            ],
+            [
+              25.468751907348633,
+              17.500001907348633
+            ],
+            [
+              25.312501907348633,
+              17.291667938232422
+            ],
+            [
+              25.312501907348633,
+              16.875
+            ],
+            [
+              25.0,
+              16.45833396911621
+            ],
+            [
+              25.0,
+              16.25
+            ],
+            [
+              24.84375,
+              16.04166603088379
+            ],
+            [
+              24.84375,
+              15.833334922790527
+            ],
+            [
+              24.6875,
+              15.625
+            ],
+            [
+              24.6875,
+              15.416666984558105
+            ],
+            [
+              24.0625,
+              14.583333015441895
+            ],
+            [
+              24.0625,
+              14.375000953674316
+            ],
+            [
+              23.90625,
+              14.166666984558105
+            ],
+            [
+              23.750001907348633,
+              14.166666984558105
+            ],
+            [
+              22.65625,
+              12.708334922790527
+            ],
+            [
+              22.5,
+              12.708334922790527
+            ],
+            [
+              21.875,
+              11.875000953674316
+            ],
+            [
+              21.71875,
+              11.875000953674316
+            ],
+            [
+              21.25,
+              11.25
+            ],
+            [
+              21.25,
+              11.041666984558105
+            ],
+            [
+              21.09375,
+              10.833333015441895
+            ],
+            [
+              21.09375,
+              9.791666984558105
+            ],
+            [
+              20.9375,
+              9.583333015441895
+            ],
+            [
+              20.9375,
+              8.541666984558105
+            ],
+            [
+              20.78125,
+              8.333333969116211
+            ],
+            [
+              20.78125,
+              7.708333492279053
+            ],
+            [
+              20.625001907348633,
+              7.500000476837158
+            ],
+            [
+              20.625001907348633,
+              7.291666507720947
+            ],
+            [
+              20.46875,
+              7.083333492279053
+            ],
+            [
+              20.46875,
+              6.25
+            ]
+          ],
+          "polygonlabels": [
+            "Person"
+          ]
+        }
+      }
+    ],
+    "score": 0.6935198505719503
+  }
+]
\ No newline at end of file
diff --git a/label_studio_ml/examples/yolo/tests/test_polygon_labels.py b/label_studio_ml/examples/yolo/tests/test_polygon_labels.py
new file mode 100644
index 000000000..9bf7437ca
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_polygon_labels.py
@@ -0,0 +1,60 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+
+```bash
+pip install -r requirements-test.txt
+```
+"""
+
+import pytest
+import json
+
+from label_studio_ml.utils import compare_nested_structures
+from .test_common import client, load_file, TEST_DIR
+
+
+label_configs = [
+    # test 1: one control tag with rectangle labels
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <PolygonLabels name="label" toName="image" score_threshold="0.6">
+        <Label value="Person" background="green"/>
+        <Label value="Truck" background="blue"/>
+      </PolygonLabels>
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1: one control tag with rectangle labels
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+]
+
+expected = [
+    # test 1: one control tag with rectangle labels
+    load_file(TEST_DIR + "/test_polygon_labels.json")
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, expect", zip(label_configs, tasks, expected)
+)
+def test_polygonlabels_predict(client, label_config, task, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+    response = client.post(
+        "/predict", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect)
diff --git a/label_studio_ml/examples/yolo/tests/test_rectangle_labels.py b/label_studio_ml/examples/yolo/tests/test_rectangle_labels.py
new file mode 100644
index 000000000..90fd7d1a9
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_rectangle_labels.py
@@ -0,0 +1,164 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+
+```bash
+pip install -r requirements-test.txt
+```
+"""
+
+import pytest
+import json
+
+from label_studio_ml.utils import compare_nested_structures
+from .test_common import client
+
+
+label_configs = [
+    # test 1: one control tag with rectangle labels
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <RectangleLabels name="label" toName="image" score_threshold="0.25">
+        <Label value="Airplane" background="green"/>
+        <Label value="Car" background="blue" predicted_values="car, truck"/>
+      </RectangleLabels>
+    </View>
+    """,
+    # test 2: two control tags with rectangle labels and two images
+    """
+    <View>
+      <Image name="image" value="$image"/>
+      <RectangleLabels name="label" toName="image" score_threshold="0.30">
+        <Label value="Airplane" background="green"/>
+        <Label value="Car" background="blue" predicted_values="car, truck"/>
+      </RectangleLabels>
+      
+      <Image name="image2" value="$image2"/>
+      <RectangleLabels name="label2" toName="image2" score_threshold="0.90">
+        <Label value="Person" background="green"/>
+        <Label value="Animal" background="blue" predicted_values="cat,dog"/>
+      </RectangleLabels>
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1: one control tag with rectangle labels
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+    # test 2: two control tags with rectangle labels and two images
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg",
+            "image2": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg",
+        }
+    },
+]
+
+expected = [
+    # test 1: one control tag with rectangle labels
+    [
+        {
+            "model_version": "yolo",
+            "result": [
+                {
+                    "from_name": "label",
+                    "score": 0.5791077017784119,
+                    "to_name": "image",
+                    "type": "rectanglelabels",
+                    "value": {
+                        "height": 77.13761925697327,
+                        "rectanglelabels": ["Car"],
+                        "width": 69.33701038360596,
+                        "x": 21.9377338886261,
+                        "y": 7.984769344329834,
+                    },
+                },
+                {
+                    "from_name": "label",
+                    "score": 0.31354132294654846,
+                    "to_name": "image",
+                    "type": "rectanglelabels",
+                    "value": {
+                        "height": 25.369155406951904,
+                        "rectanglelabels": ["Car"],
+                        "width": 18.623733520507812,
+                        "x": 81.27312660217285,
+                        "y": 0.10521858930587769,
+                    },
+                },
+            ],
+            "score": 0.44632451236248016,
+        }
+    ],
+    # test 2: two control tags with rectangle labels and two images
+    [
+        {
+            "model_version": "yolo",
+            "result": [
+                {
+                    "from_name": "label",
+                    "score": 0.5791077017784119,
+                    "to_name": "image",
+                    "type": "rectanglelabels",
+                    "value": {
+                        "height": 77.13761925697327,
+                        "rectanglelabels": ["Car"],
+                        "width": 69.33701038360596,
+                        "x": 21.9377338886261,
+                        "y": 7.984769344329834,
+                    },
+                },
+                {
+                    "from_name": "label",
+                    "score": 0.31354132294654846,
+                    "to_name": "image",
+                    "type": "rectanglelabels",
+                    "value": {
+                        "height": 25.369155406951904,
+                        "rectanglelabels": ["Car"],
+                        "width": 18.623733520507812,
+                        "x": 81.27312660217285,
+                        "y": 0.10521858930587769,
+                    },
+                },
+                {
+                    "from_name": "label2",
+                    "score": 0.9059886932373047,
+                    "to_name": "image2",
+                    "type": "rectanglelabels",
+                    "value": {
+                        "height": 39.60925042629242,
+                        "rectanglelabels": ["Person"],
+                        "width": 10.503808408975601,
+                        "x": 89.45398144423962,
+                        "y": 6.985808908939362,
+                    },
+                },
+            ],
+            "score": 0.5995459059874216,
+        }
+    ],
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, expect", zip(label_configs, tasks, expected)
+)
+def test_rectanglelabels_predict(client, label_config, task, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+    response = client.post(
+        "/predict", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect)
diff --git a/label_studio_ml/examples/yolo/tests/test_rectangle_labels_obb.py b/label_studio_ml/examples/yolo/tests/test_rectangle_labels_obb.py
new file mode 100644
index 000000000..b7ca05c3f
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_rectangle_labels_obb.py
@@ -0,0 +1,89 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+
+```bash
+pip install -r requirements-test.txt
+```
+"""
+
+import pytest
+import json
+
+from label_studio_ml.utils import compare_nested_structures
+from .test_common import client
+
+
+label_configs = [
+    # test 1: one control tag with rectangle labels
+    """
+    <View>
+      <Header value="Select label and click the image to start"/>
+      <Image name="image" value="$image" zoom="true"/>
+      
+      <RectangleLabels name="rect" toName="image"
+                     score_threshold="0.1" model_obb="true">
+        <Label value="plane" background="red" 
+            predicted_values="plane,helicopter"/>
+        <Label value="vehicle" background="blue" 
+            predicted_values="ship,storage tank,bridge,large vehicle,small vehicle"/>
+      </RectangleLabels>
+      
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1: one control tag with rectangle labels
+    {
+        "data": {
+            "image": "https://s3.amazonaws.com/htx-pub/datasets/mmdetection-ml-test/001bebecea382500.jpg"
+        }
+    },
+]
+
+expected = [
+    # test 1: one control tag with rectangle labels
+    [
+        {
+            "model_version": "yolo",
+            "result": [
+                {
+                    "from_name": "rect",
+                    "score": 0.32253125309944153,
+                    "to_name": "image",
+                    "type": "rectanglelabels",
+                    "value": {
+                        "height": 3.3118023546502084,
+                        "original_height": 576,
+                        "original_width": 768,
+                        "rectanglelabels": ["vehicle"],
+                        "rotation": -89.43998820538127,
+                        "width": 2.2955212735479535,
+                        "x": 1.9985803710085965,
+                        "y": 10.487648558804944,
+                    },
+                }
+            ],
+            "score": 0.32253125309944153,
+        }
+    ]
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, expect", zip(label_configs, tasks, expected)
+)
+def test_rectanglelabels_obb_predict(client, label_config, task, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+    response = client.post(
+        "/predict", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect)
diff --git a/label_studio_ml/examples/yolo/tests/test_timeline_labels.py b/label_studio_ml/examples/yolo/tests/test_timeline_labels.py
new file mode 100644
index 000000000..ad10fe164
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_timeline_labels.py
@@ -0,0 +1,74 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+"""
+
+import pytest
+import json
+
+from label_studio_ml.utils import compare_nested_structures
+from model import YOLO
+from .test_common import client, load_file, TEST_DIR
+from unittest import mock
+
+
+label_configs = [
+    # test 1: one control tag with video timeline labels
+    """
+    <View>
+       <TimelineLabels name="videoLabels" toName="video">
+         <Label value="Car" predicted_values="racer, cab"/>
+         <Label value="Airplane" background="red"/>
+       </TimelineLabels>
+       <Video name="video" value="$video" framerate="25.0" height="400"/>
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1
+    {"data": {"video": "tests/opossum_snow_short.mp4"}},
+]
+
+yolo_results = [
+    None # load_file(TEST_DIR + "/timeline_labels.pickle")
+]
+
+expected = [
+    # test 1: one control tag with rectangle labels
+    None # load_file(TEST_DIR + "/timeline_labels_1.json"),
+]
+
+
+@pytest.mark.skip(reason="Not yet implemented")
+@pytest.mark.parametrize(
+    "label_config, task, yolo_result, expect",
+    zip(label_configs, tasks, yolo_results, expected),
+)
+def test_timelinelabels_predict(client, label_config, task, yolo_result, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+
+    # mock yolo model.track, because it takes too different results from run to run
+    # also track is a heavy operation, and it might take too much time for tests
+    if yolo_result:
+        with mock.patch("ultralytics.YOLO.track") as mock_yolo:
+            mock_yolo.return_value = yolo_result
+            response = client.post(
+                "/predict", data=json.dumps(data), content_type="application/json"
+            )
+
+    # don't mock if no yolo_result
+    else:
+        response = client.post(
+            "/predict", data=json.dumps(data), content_type="application/json"
+        )
+
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect, rel=1e-3)
+
diff --git a/label_studio_ml/examples/yolo/tests/test_video_rectangle.py b/label_studio_ml/examples/yolo/tests/test_video_rectangle.py
new file mode 100644
index 000000000..54f57dc22
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/test_video_rectangle.py
@@ -0,0 +1,182 @@
+"""
+This file contains tests for the API of your model. You can run these tests by installing test requirements:
+"""
+
+import os
+import pytest
+import json
+import yaml
+
+from label_studio_ml.utils import compare_nested_structures
+from model import YOLO
+from .test_common import client, load_file, TEST_DIR
+from unittest import mock
+
+
+label_configs = [
+    # test 1: one control tag with video rectangle
+    """
+    <View>
+       <Labels name="videoLabels" toName="video" allowEmpty="true">
+         <Label value="person" background="blue"/>
+       </Labels>
+       
+       <!-- Please specify FPS carefully, it will be used for all project videos -->
+       <Video name="video" value="$video" framerate="25.0"/>
+       <VideoRectangle name="box" toName="video" botsort_track_high_thresh="0.1" botsort_track_low_thresh="0.1" />
+    </View>
+    """,
+    # test 2: video rectangle without botsort parameters
+    """
+    <View>
+       <Labels name="videoLabels" toName="video" allowEmpty="true">
+         <Label value="person" background="blue"/>
+       </Labels>
+    
+       <!-- Please specify FPS carefully, it will be used for all project videos -->
+       <Video name="video" value="$video" framerate="25.0"/>
+       <VideoRectangle name="box" toName="video" />
+    </View>
+    """,
+]
+
+tasks = [
+    # test 1: one control tag with rectangle labels
+    {"data": {"video": "tests/opossum_snow_short.mp4"}},
+    # test 2: one control tag with rectangle labels
+    {"data": {"video": "tests/opossum_snow_short.mp4"}},
+]
+
+yolo_results = [
+    load_file(TEST_DIR + "/opossum_snow_short.pickle"),
+    None
+]
+
+expected = [
+    # test 1: one control tag with rectangle labels
+    load_file(TEST_DIR + "/opossum_snow_short_1.json"),
+    load_file(TEST_DIR + "/opossum_snow_short_2.json"),
+]
+
+
+@pytest.mark.parametrize(
+    "label_config, task, yolo_result, expect",
+    zip(label_configs, tasks, yolo_results, expected),
+)
+def test_rectanglelabels_predict(client, label_config, task, yolo_result, expect):
+    data = {"schema": label_config, "project": "42"}
+    response = client.post(
+        "/setup", data=json.dumps(data), content_type="application/json"
+    )
+    assert response.status_code == 200, "Error while setup: " + str(response.content)
+
+    data = {"tasks": [task], "label_config": label_config}
+
+    # mock yolo model.track, because it takes too different results from run to run
+    # also track is a heavy operation, and it might take too much time for tests
+    if yolo_result:
+        with mock.patch("ultralytics.YOLO.track") as mock_yolo:
+            mock_yolo.return_value = yolo_result
+            response = client.post(
+                "/predict", data=json.dumps(data), content_type="application/json"
+            )
+
+    # don't mock if no yolo_result
+    else:
+        response = client.post(
+            "/predict", data=json.dumps(data), content_type="application/json"
+        )
+
+    assert response.status_code == 200, "Error while predict"
+    data = response.json
+    compare_nested_structures(data["results"], expect, rel=1e-3)
+
+
+def test_create_video_rectangles():
+    """How to create pickle?
+    1. Make a break point at the first line of create_video_rectangles()
+    2. Run test_rectanglelabels_predict() test
+    3. On the breakpoint:
+        3.1 import pickle
+        3.2 for r in results: r.orig_img = []
+        3.2 with open('model_track_results.pickle', 'wb') as f: pickle.dump(results, f)
+    """
+    ml = YOLO(project_id="42", label_config=label_configs[0])
+    control_models = ml.detect_control_models()
+    regions = control_models[0].create_video_rectangles(
+        yolo_results[0], "tests/opossum_snow_short.mp4"
+    )
+
+    predictions = expected[0]
+    assert regions == predictions[0]["result"]
+
+
+def test_update_tracker_params_with_real_config():
+    tmp_path = os.path.dirname(__file__)
+    label_config = """
+    <View>
+       <Labels name="videoLabels" toName="video" allowEmpty="true">
+         <Label value="person" background="blue"/>
+       </Labels>
+
+       <!-- Please specify FPS carefully, it will be used for all project videos -->
+       <Video name="video" value="$video" framerate="25.0"/>
+       <VideoRectangle name="box" toName="video" 
+           botsort_track_high_thresh="0.6" 
+           botsort_track_low_thresh="0.4" 
+           botsort_new_track_thresh="0.3" 
+           botsort_track_buffer="50" 
+           botsort_match_thresh="0.85" 
+           botsort_fuse_score="false" 
+           botsort_gmc_method="none" />
+    </View>
+    """
+
+    # Initialize the model with the label config
+    ml = YOLO(project_id="42", label_config=label_config)
+    control_models = ml.detect_control_models()
+    video_rectangle_model = control_models[0]
+
+    # Mock original botsort.yaml content
+    original_yaml_content = """
+        tracker_type: botsort
+        track_high_thresh: 0.1
+        track_low_thresh: 0.1
+        new_track_thresh: 0.1
+        track_buffer: 30
+        match_thresh: 0.8
+        fuse_score: true
+        gmc_method: sparseOptFlow
+        proximity_thresh: 0.5
+        appearance_thresh: 0.25
+        with_reid: false
+    """
+
+    # Create a temporary YAML file to simulate the original config
+    original_yaml_path = f"{tmp_path}/botsort.yaml"
+    with open(original_yaml_path, "w") as file:
+        file.write(original_yaml_content)
+
+    # Update tracker parameters based on the labeling config
+    new_yaml_path = video_rectangle_model.update_tracker_params(
+        original_yaml_path, "botsort_"
+    )
+
+    # Check that the new YAML file was created
+    assert os.path.exists(new_yaml_path), "The new YAML file was not created."
+
+    # Load the new YAML file
+    with open(new_yaml_path, "r") as file:
+        updated_config = yaml.safe_load(file)
+
+    # Verify that the parameters were correctly updated
+    assert updated_config["track_high_thresh"] == 0.6
+    assert updated_config["track_low_thresh"] == 0.4
+    assert updated_config["new_track_thresh"] == 0.3
+    assert updated_config["track_buffer"] == 50
+    assert updated_config["match_thresh"] == 0.85
+    assert updated_config["fuse_score"] == False  # Boolean comparison
+    assert updated_config["gmc_method"] == "none"
+
+    # Clean up: remove the temporary YAML file
+    os.remove(new_yaml_path)
diff --git a/label_studio_ml/examples/yolo/tests/tmp.txt b/label_studio_ml/examples/yolo/tests/tmp.txt
new file mode 100644
index 000000000..3cc762b55
--- /dev/null
+++ b/label_studio_ml/examples/yolo/tests/tmp.txt
@@ -0,0 +1 @@
+""
\ No newline at end of file
diff --git a/label_studio_ml/model.py b/label_studio_ml/model.py
index 7f287949f..f669c64cc 100644
--- a/label_studio_ml/model.py
+++ b/label_studio_ml/model.py
@@ -78,7 +78,10 @@ def __init__(self, project_id: Optional[str] = None, label_config=None):
             project_id (str, optional): The project ID. Defaults to None.
         """
         self.project_id = project_id or ''
-        self.use_label_config(label_config)
+        if label_config is not None:
+            self.use_label_config(label_config)
+        else:
+            logger.warning('Label config is not provided')
 
         # set initial model version
         if not self.model_version:
@@ -321,6 +324,86 @@ def get_first_tag_occurence(
             to_name_filter=to_name_filter
         )
 
+    def build_label_map(self, tag_name: str, names: List[str]) -> Dict[str, str]:
+        """Build a mapping between model label names and the label names defined in the Label Studio configuration.
+
+        This function creates a dictionary that maps predicted label names from a machine learning model
+        (e.g., "car", "truck") to the corresponding label names used in Label Studio (e.g., "Car").
+
+        The mapping is primarily based on the `predicted_values` attribute within each <Label> tag in the
+        Label Studio configuration. If a match is not found via `predicted_values`, the function attempts
+        to match the labels directly by name, either by exact match or by a case-insensitive comparison.
+
+        Args:
+            tag_name (str): The name of the Label Studio tag that contains the labels (<Label>).
+            names (List[str]): A list of label names used in the model, which need to be mapped
+                               to the corresponding labels in Label Studio.
+
+        Returns:
+            Dict[str, str]: A dictionary where the keys are the label names from the model,
+                            and the values are the corresponding label names from the Label Studio configuration.
+                            The resulting dictionary is intended to facilitate the integration of model predictions
+                            with Label Studio, ensuring that the labels used in predictions match the labels
+                            configured in the tool.
+
+        Example:
+            Given a Label Studio configuration like:
+
+            <View>
+              <Image name="image" value="$image"/>
+              <RectangleLabels name="label" toName="image" score_threshold="0.25">
+                <Label value="Airplane" background="green"/>
+                <Label value="Car" background="blue" predicted_values="car, truck"/>
+              </RectangleLabels>
+            </View>
+
+            And a list of model names like:
+            ["car", "truck", "airplane"]
+
+            The function would return:
+            {
+                'car': 'Car',
+                'truck': 'Car',
+                'airplane': 'Airplane'
+            }
+
+        Notes:
+            - If a label in `predicted_values` is not found in the model's labels, a warning is logged.
+            - If `predicted_values` are not present or are empty, the function will attempt to map the label
+              directly using the label's name, considering both case-sensitive and case-insensitive matches.
+        """
+        label_map = {}
+        labels_attrs = self.label_interface.get_control(tag_name).labels_attrs
+
+        model_labels = list(names)
+        model_labels_lower = [label.lower() for label in model_labels]
+        logger.debug(f"Labels supported by model for {tag_name}: {names}")
+
+        if labels_attrs:
+            for ls_label, label_tag in labels_attrs.items():
+                # try to find `predicted_values` in Label tags
+                predicted_values = label_tag.attr.get("predicted_values", "").split(",")
+                matched = False
+                for value in predicted_values:
+                    value = value.strip()  # remove spaces at the beginning and at the end
+                    if value and value in names:  # check if value is in model labels
+                        if value not in model_labels:
+                            logger.warning(f'Predicted value "{value}" is not in model labels')
+                        label_map[value] = ls_label
+                        matched = True
+
+                # no `predicted_values`, use common Label's `value` attribute
+                if not matched:
+                    # model has the same label
+                    if ls_label in model_labels:
+                        label_map[ls_label] = ls_label
+                    # model has the same lower name
+                    elif ls_label.lower() in model_labels_lower:
+                        label_map[ls_label.lower()] = ls_label
+
+        logger.debug(f"Model Labels <=> Label Studio Labels:\n{label_map}")
+        return label_map
+
 
 def get_all_classes_inherited_LabelStudioMLBase(script_file):
     """
diff --git a/label_studio_ml/server.py b/label_studio_ml/server.py
index bdbf3d435..c5a05514e 100644
--- a/label_studio_ml/server.py
+++ b/label_studio_ml/server.py
@@ -196,7 +196,7 @@ def deploy_to_gcp(args):
     # check if auth token exists
     auth_token = subprocess.check_output(' '.join(["gcloud", "auth", "print-identity-token"]), shell=True)
     if not auth_token:
-        raise PermissionError("You are not authentificated in gcloud! Please run gcloud auth login.")
+        raise PermissionError("You are not authenticated in gcloud! Please run gcloud auth login.")
     # configurate project
     subprocess.check_output(' '.join(["gcloud", "config", "set", "project", project_id]), shell=True)
     # deploy service
diff --git a/label_studio_ml/utils.py b/label_studio_ml/utils.py
index ee129ddc0..31aaada6a 100644
--- a/label_studio_ml/utils.py
+++ b/label_studio_ml/utils.py
@@ -160,8 +160,19 @@ def is_preload_needed(url):
     )
 
 
-if __name__ == "__main__":
-    c = InMemoryLRUDictCache(2)
-    c.put(1, 1)
-    c.put(2,2)
-    print(c.cache)
+def compare_nested_structures(a, b, path="", rel=1e-4):
+    from pytest import approx  # pytest is optional package, use it inside of this func
+
+    """Compare two dicts or list with approx() for float values"""
+    if isinstance(a, dict) and isinstance(b, dict):
+        assert a.keys() == b.keys(), f"Keys mismatch at {path}"
+        for key in a.keys():
+            compare_nested_structures(a[key], b[key], path + "." + str(key))
+    elif isinstance(a, list) and isinstance(b, list):
+        assert len(a) == len(b), f"List size mismatch at {path}"
+        for i, (act_item, exp_item) in enumerate(zip(a, b)):
+            compare_nested_structures(act_item, exp_item, path + f"[{i}]")
+    elif isinstance(a, float) and isinstance(b, float):
+        assert a == approx(b, rel=rel), f"Mismatch at {path}"
+    else:
+        assert a == b, f"Mismatch at {path}"
diff --git a/requirements.txt b/requirements.txt
index e85189c1c..0fd3caded 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,5 +3,5 @@ colorama~=0.4
 requests~=2.31
 semver~=3.0.2
 pillow~=10.3
-label-studio-sdk==1.0.2
-# label-studio-sdk @ git+https://github.com/HumanSignal/label-studio-sdk.git
+# label-studio-sdk==1.0.2
+label-studio-sdk @ git+https://github.com/HumanSignal/label-studio-sdk.git
diff --git a/tests/test_build_label_map.py b/tests/test_build_label_map.py
new file mode 100644
index 000000000..5c3600192
--- /dev/null
+++ b/tests/test_build_label_map.py
@@ -0,0 +1,127 @@
+import pytest
+
+from label_studio_ml.model import LabelStudioMLBase
+
+
+@pytest.mark.parametrize(
+    "label_config, model_names, tag_name, expected",
+    [
+        # Test case 1: Empty labels_attrs
+        (
+            """<View>
+                <Image name="image" value="$image"/>
+                <RectangleLabels name="label" toName="image">
+                </RectangleLabels>
+            </View>""",
+            ['car', 'truck'],
+            "label",
+            {}
+        ),
+        # Test case 2: With predicted_values
+        (
+            """<View>
+                <Image name="image" value="$image"/>
+                <RectangleLabels name="label" toName="image">
+                    <Label value="Car" predicted_values="car,truck"/>
+                    <Label value="Airplane" predicted_values="airplane"/>
+                </RectangleLabels>
+            </View>""",
+            ['car', 'truck', 'airplane'],
+            "label",
+            {
+                'car': 'Car',
+                'truck': 'Car',
+                'airplane': 'Airplane'
+            }
+        ),
+        # Test case 3: Without predicted_values
+        (
+            """<View>
+                <Image name="image" value="$image"/>
+                <RectangleLabels name="label" toName="image">
+                    <Label value="Car"/>
+                    <Label value="Airplane"/>
+                </RectangleLabels>
+            </View>""",
+            ['car', 'airplane'],
+            "label",
+            {
+                'car': 'Car',
+                'airplane': 'Airplane'
+            }
+        ),
+        # Test case 4: Partial matching labels
+        (
+            """<View>
+                <Image name="image" value="$image"/>
+                <RectangleLabels name="label" toName="image">
+                    <Label value="Car" predicted_values="car"/>
+                    <Label value="Plane" predicted_values="airplane"/>
+                    <Label value="Flower"/>
+                </RectangleLabels>
+            </View>""",
+            ['car', 'airplane', 'bicycle'],
+            "label",
+            {
+                'car': 'Car',
+                'airplane': 'Plane'
+            }
+        ),
+        # Test case 5: Mixed matching labels
+        (
+            """<View>
+                <Image name="image" value="$image"/>
+                <RectangleLabels name="label" toName="image">
+                    <Label value="Car" predicted_values="car"/>
+                    <Label value="Plane" predicted_values="airplane"/>
+                    <Label value="Bicycle"/>
+                </RectangleLabels>
+            </View>""",
+            ['car', 'airplane', 'bicycle'],
+            "label",
+            {
+                'car': 'Car',
+                'airplane': 'Plane',
+                'bicycle': 'Bicycle'
+            }
+        ),
+        # Test case 6: Mixed matching labels with capitalized char in model names and one space
+        (
+                """<View>
+                    <Image name="image" value="$image"/>
+                    <RectangleLabels name="label" toName="image">
+                        <Label value="Car" predicted_values="car"/>
+                        <Label value="Plane" predicted_values="airplane"/>
+                        <Label value="Bicycle space"/>
+                    </RectangleLabels>
+                </View>""",
+                ['car', 'airplane', 'Bicycle space'],
+                "label",
+                {
+                    'car': 'Car',
+                    'airplane': 'Plane',
+                    'Bicycle space': 'Bicycle space'
+                }
+        ),
+        # Test case 7: Label not in model
+        (
+            """<View>
+                <Image name="image" value="$image"/>
+                <RectangleLabels name="label" toName="image">
+                    <Label value="Car" predicted_values="car,truck"/>
+                    <Label value="Airplane" predicted_values="jet"/>
+                </RectangleLabels>
+            </View>""",
+            ['car', 'truck'],
+            "label",
+            {
+                'car': 'Car',
+                'truck': 'Car'
+            }
+        ),
+    ]
+)
+def test_build_label_map(label_config, model_names, tag_name, expected):
+    obj = LabelStudioMLBase(project_id="42", label_config=label_config)
+    result = obj.build_label_map(tag_name, model_names)
+    assert result == expected