Refine RAM links

README.md

@@ -25,7 +25,7 @@ The **core idea** behind this project is to **combine the strengths of different
 - **`2023/06/14`** Support [RAM-Grounded-SAM & SAM-HQ](./automatic_label_ram_demo.py) and update [Simple Automatic Label Demo](./automatic_label_ram_demo.py) to support [RAM](https://github.com/OPPOMKLab/recognize-anything), setting up a strong automatic annotation pipeline.
 - **`2023/06/13`** Checkout the [Autodistill: Train YOLOv8 with ZERO Annotations](https://youtu.be/gKTYMfwPo4M) tutorial to learn how to use Grounded-SAM + [Autodistill](https://github.com/autodistill/autodistill) for automated data labeling and real-time model training.
 - **`2023/06/13`** Support [SAM-HQ](https://github.com/SysCV/sam-hq) in [Grounded-SAM Demo](#running_man-grounded-sam-detect-and-segment-everything-with-text-prompt) for higher quality prediction.
-- **`2023/06/12`** Support [RAM-Grounded-SAM](#label-grounded-sam-with-ram-or-tag2text-for-automatic-labeling) for strong automatic labeling pipeline! Thanks for [Recognize-Anything](https://github.com/xinyu1205/Recognize_Anything-Tag2Text).
+- **`2023/06/12`** Support [RAM-Grounded-SAM](#label-grounded-sam-with-ram-or-tag2text-for-automatic-labeling) for strong automatic labeling pipeline! Thanks for [Recognize-Anything](https://github.com/OPPOMKLab/recognize-anything).
 - **`2023/06/01`** Our Grounded-SAM has been accepted to present a **demo** at [ICCV 2023](https://iccv2023.thecvf.com/)! See you in Paris!
 - **`2023/05/23`**: Support `Image-Referring-Segment`, `Audio-Referring-Segment` and `Text-Referring-Segment` in [ImageBind-SAM](./playground/ImageBind_SAM/).
 - **`2023/05/03`**: Checkout the [Automated Dataset Annotation and Evaluation with GroundingDINO and SAM](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino-and-sam.ipynb) which is an amazing tutorial on automatic labeling! Thanks a lot for [Piotr Skalski](https://github.com/SkalskiP) and [Roboflow](https://github.com/roboflow/notebooks)!
@@ -77,10 +77,10 @@ Here we provide some background knowledge that you may need to know before tryin
 | [Grounding DINO](https://arxiv.org/abs/2303.05499) | ![](https://github.com/IDEA-Research/GroundingDINO/blob/main/.asset/hero_figure.png?raw=True) | A strong zero-shot detector which is capable of to generate high quality boxes and labels with free-form text. | [[Github](https://github.com/IDEA-Research/GroundingDINO)] <br> [[Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] |
 | [OSX](http://arxiv.org/abs/2303.16160) | ![](https://github.com/IDEA-Research/OSX/blob/main/assets/demo_video.gif?raw=True) | A strong and efficient one-stage motion capture method to generate high quality 3D human mesh from monucular image. OSX also releases a large-scale upper-body dataset UBody for a more accurate reconstrution in the upper-body scene. | [[Github](https://github.com/IDEA-Research/OSX)] <br> [[Page](https://osx-ubody.github.io/)] <br> [[Video](https://osx-ubody.github.io/)] <br> [[Data](https://docs.google.com/forms/d/e/1FAIpQLSehgBP7wdn_XznGAM2AiJPiPLTqXXHw5uX9l7qeQ1Dh9HoO_A/viewform)] |
 | [Stable-Diffusion](https://arxiv.org/abs/2112.10752) | ![](https://github.com/CompVis/stable-diffusion/blob/main/assets/stable-samples/txt2img/merged-0006.png?raw=True) | A super powerful open-source latent text-to-image diffusion model | [[Github](https://github.com/CompVis/stable-diffusion)] <br> [[Page](https://ommer-lab.com/research/latent-diffusion-models/)] |
-| [RAM](https://recognize-anything.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/localization_and_recognition.jpg) | RAM is an image tagging model, which can recognize any common category with high accuracy. | [[Github](https://github.com/xinyu1205/Recognize_Anything-Tag2Text)] <br> [[Demo](https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text)] |
+| [RAM](https://recognize-anything.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/localization_and_recognition.jpg) | RAM is an image tagging model, which can recognize any common category with high accuracy. | [[Github](https://github.com/OPPOMKLab/recognize-anything)] <br> [[Demo](https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text)] |
 | [BLIP](https://arxiv.org/abs/2201.12086) | ![](https://github.com/salesforce/LAVIS/raw/main/docs/_static/logo_final.png) | A wonderful language-vision model for image understanding. | [[GitHub](https://github.com/salesforce/LAVIS)] |
 | [Visual ChatGPT](https://arxiv.org/abs/2303.04671) | ![](https://github.com/microsoft/TaskMatrix/raw/main/assets/figure.jpg) | A wonderful tool that connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. | [[Github](https://github.com/microsoft/TaskMatrix)] <br> [[Demo](https://huggingface.co/spaces/microsoft/visual_chatgpt)] |
-| [Tag2Text](https://tag2text.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/tag2text_framework.png) | An efficient and controllable vision-language model which can simultaneously output superior image captioning and image tagging. | [[Github](https://github.com/xinyu1205/Tag2Text)] <br> [[Demo](https://huggingface.co/spaces/xinyu1205/Tag2Text)] |
+| [Tag2Text](https://tag2text.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/tag2text_framework.png) | An efficient and controllable vision-language model which can simultaneously output superior image captioning and image tagging. | [[Github](https://github.com/OPPOMKLab/recognize-anything)] <br> [[Demo](https://huggingface.co/spaces/xinyu1205/Tag2Text)] |
 | [VoxelNeXt](https://arxiv.org/abs/2303.11301) | ![](https://github.com/dvlab-research/VoxelNeXt/raw/master/docs/sequence-v2.gif) | A clean, simple, and fully-sparse 3D object detector, which predicts objects directly upon sparse voxel features. | [[Github](https://github.com/dvlab-research/VoxelNeXt)] 
 
 </div>
@@ -439,7 +439,7 @@ python gradio_app.py
 
 
 ### :label: Grounded-SAM with RAM or Tag2Text for Automatic Labeling
-[**The Recognize Anything Model (RAM) and Tag2Text**](https://github.com/xinyu1205/Recognize_Anything-Tag2Text) exhibits **exceptional recognition abilities**, in terms of **both accuracy and scope**.
+[**The Recognize Anything Model (RAM) and Tag2Text**](https://github.com/OPPOMKLab/recognize-anything) exhibits **exceptional recognition abilities**, in terms of **both accuracy and scope**.
 
 It is seamlessly linked to generate pseudo labels automatically as follows:
 1. Use RAM/Tag2Text to generate tags.