Grounded SAM 2 does not introduce significant methodological changes compared to Grounded SAM. Both approaches leverage the capabilities of open-world models to address complex visual tasks. Consequently, we try to simplify the code implementation in this repository, aiming to enhance user convenience.

Installation
Grounded-SAM-2 Demo
Citation

Installation

Since we need the CUDA compilation environment to compile the Deformable Attention operator used in Grounding DINO, we need to check whether the CUDA environment variables have been set correctly (which you can refer to Grounding DINO Installation for more details). You can set the environment variable manually as follows if you want to build a local GPU environment for Grounding DINO to run Grounded SAM 2:

export CUDA_HOME=/path/to/cuda-12.1/

Install Segment Anything 2:

pip install -e .

Install Grounding DINO:

pip install --no-build-isolation -e grounding_dino

Downgrade the version of the supervision library to 0.6.0 to use its original API for visualization (we will update our code to be compatible with the latest version of supervision in the future release):

pip install supervision==0.6.0

Download the pretrained SAM 2 checkpoints:

cd checkpoints
bash download_ckpts.sh

Download the pretrained Grounding DINO checkpoints:

cd gdino_checkpoints
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth

Grounded-SAM-2 Demo

Grounded-SAM-2 Image Demo (with Grounding DINO)

Note that Grounding DINO has already been supported in Huggingface, so we provide two choices for running Grounded-SAM-2 model:

Use huggingface API to inference Grounding DINO (which is simple and clear)

python grounded_sam2_hf_model_demo.py

Load local pretrained Grounding DINO checkpoint and inference with Grounding DINO original API (make sure you've already downloaded the pretrained checkpoint)

python grounded_sam2_local_demo.py

Grounded-SAM-2 Image Demo (with Grounding DINO 1.5 & 1.6)

We've already released our most capable open-set detection model Grounding DINO 1.5 & 1.6, which can be combined with SAM 2 for stronger open-set detection and segmentation capability. You can apply the API token first and run Grounded-SAM-2 with Grounding DINO 1.5 as follows:

Install the latest DDS cloudapi:

pip install dds-cloudapi-sdk

Apply your API token from our official website here: request API token.

python grounded_sam2_gd1.5_demo.py

Grounded-SAM-2 Video Object Tracking Demo

Based on the strong tracking capability of SAM 2, we can combined it with Grounding DINO for open-set object segmentation and tracking. You can run the following scripts to get the tracking results with Grounded-SAM-2:

python grounded_sam2_tracking_demo.py

The tracking results of each frame will be saved in ./tracking_results
The video will be save as children_tracking_demo_video.mp4
You can refine this file with different text prompt and video clips yourself to get more tracking results.
We only prompt the first video frame with Grounding DINO here for simple usage.

Uniform Point Sampling for Stable Segmentation Results in Video Demos based on SAM 2 Image Predictor

We have observed that the video predictor in SAM 2 currently supports only point prompts (please feel free to point out any updates or functionalities we may have overlooked during development). However, Grounding DINO provides box prompts, which need to be converted into point prompts for use in video tracking. A straightforward approach is to directly sample the center point of the box as a point prompt. Nevertheless, this method may encounter certain issues in practical testing scenarios. To get a more stable segmentation results, we reuse the SAM 2 image predictor to get the prediction mask for each object first, then we uniformly sample points from the prediction mask to prompt SAM 2 video predictor.

Citation

If you find this project helpful for your research, please consider citing the following BibTeX entry.

@misc{ravi2024sam2segmentimages,
      title={SAM 2: Segment Anything in Images and Videos}, 
      author={Nikhila Ravi and Valentin Gabeur and Yuan-Ting Hu and Ronghang Hu and Chaitanya Ryali and Tengyu Ma and Haitham Khedr and Roman Rädle and Chloe Rolland and Laura Gustafson and Eric Mintun and Junting Pan and Kalyan Vasudev Alwala and Nicolas Carion and Chao-Yuan Wu and Ross Girshick and Piotr Dollár and Christoph Feichtenhofer},
      year={2024},
      eprint={2408.00714},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.00714}, 
}

@article{kirillov2023segany,
  title={Segment Anything}, 
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

@article{liu2023grounding,
  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
  journal={arXiv preprint arXiv:2303.05499},
  year={2023}
}

@misc{ren2024grounded,
      title={Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks}, 
      author={Tianhe Ren and Shilong Liu and Ailing Zeng and Jing Lin and Kunchang Li and He Cao and Jiayu Chen and Xinyu Huang and Yukang Chen and Feng Yan and Zhaoyang Zeng and Hao Zhang and Feng Li and Jie Yang and Hongyang Li and Qing Jiang and Lei Zhang},
      year={2024},
      eprint={2401.14159},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

README.md

Grounded-SAM-2

Contents