Introduction to Grounded-SAM-2: Ground and Track Anything in Videos
Grounded-SAM-2 is an innovative project developed by IDEA-Research to tackle complex visual tasks in video content. The project combines the power of several advanced models, such as Grounding DINO and SAM 2, to create a robust framework capable of grounding and tracking virtually anything in video material. This setup is built on previous foundational models, aiming to simplify the process of handling complex visual information in an open-world context.
Key Features
Grounded-SAM-2 allows users to perform a variety of tasks in an intuitive and straightforward manner:
- Ground and Segment Anything: The project supports grounding and segmenting any objects within videos using Grounding DINO, its updated versions, Grounding DINO 1.5 and 1.6, and SAM 2.
- Ground and Track Anything: Similar tools are provided for tracking objects throughout video sequences, using the same models mentioned above.
- Visualization Tools: Users can take advantage of visualization capabilities based on the supervision library to detect, segment, and track objects more effectively.
A remarkable aspect of Grounded-SAM-2 is its focus on user accessibility. The project does not introduce significant methodological changes from previous versions but concentrates on making the implementation process simpler and more user-friendly.
Recent Updates
Grounded-SAM-2 has undergone several updates to enhance its functionality:
- As of October 2024, high-resolution image inference is supported, including 4K images, which are useful for detecting dense and small objects.
- The project now supports SAM-2.1 models, enhancing tracking and segmentation capabilities.
- A feature under development allows users to ground and track newly introduced objects throughout video content.
- Users can input custom video files and receive the corresponding grounding and tracking results based on specific text prompts.
Installation and Usage
The installation process for Grounded-SAM-2 can be accomplished with or without Docker:
Without Docker
- Set up a PyTorch environment using Python 3.10 and CUDA 12.1.
- Install Segment Anything 2 and Grounding DINO.
- Download pretrained checkpoints for both SAM 2 and Grounding DINO.
With Docker
- Build and run a Docker container specifically prepared for the project’s environment.
- Initiate demos directly from the Docker-operated environment.
Demos and Applications
Grounded-SAM-2 includes a wealth of demo applications to illustrate its capabilities:
- Users can experiment with grounding and tracking using different versions of Grounding DINO.
- Various prompt types (e.g., point, box, and mask) are supported, enhancing the versatility and precision of object tracking.
- The project offers specialized demos for custom video inputs and tests continuous ID tracking.
Integration with Florence-2
Grounded-SAM-2 also integrates with Florence-2, expanding its application scope to include tasks like object detection, dense region captioning, and phrase grounding. This makes it an even more robust solution for handling intricate video analysis tasks.
Overall, Grounded-SAM-2 marks a significant advancement in open-world video analysis, offering users powerful tools in an easy-to-use format to process, ground, and track elements across video content efficiently.