Object detection: Bounding box regression.

Bounding box regression

What is a bounding box?

Typically a bounding box is a set of 2 coordinates of a rectangle (upper left and lower right corners) around an area of interest, such as the dog in the image below.

For instance, in this image we have, a bounding box where

What is bounding box regression?

Is just technique to predict the coordinates of a bounding-box of a given image, learn more details in Universal Bounding Box Regression and Its Applications.

Main Idea

In order to perform bounding box regression for object detection, all we need to do is build a network architecture:

• At the head of the network, place a fully-connected layer with four neurons, corresponding to the values of the upper-left and lower-right (x, y)-coordinates.
• Given that four-neuron layer, implement a sigmoid activation function such that the outputs are returned in the range [0, 1].
• Train the model using a loss function on:
  • the input images
  • the bounding box of the object in the image.

Project Organization

---

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience