Grounded-Segment-Anything is a research-oriented project that combines powerful open-set object detection with pixel-level segmentation and subsequent creative workflows, effectively enabling detection, segmentation, and high-level vision tasks guided by free-form text prompts. The core idea behind the project is to pair Grounding DINO — a zero-shot object detector that can locate objects described by natural language — with Segment Anything Model (SAM), which can produce detailed masks for objects once they are localized. This fusion lets users provide arbitrary text descriptions (e.g., “a cat, a bicycle, or a coffee mug”), have the detection model find relevant bounding boxes, and then use SAM to generate precise segmentation masks that isolate each object in the scene.
Features
- Combines Grounding DINO detection with SAM segmentation
- Zero-shot object segmentation using free-form text prompts
- Supports demo workflows including inpainting and dataset annotation
- Modular pipeline integrating language, detection, and segmentation
- Extensions for audio or visual prompts with auxiliary models
- Useful for research and prototype interactive vision systems
Project Samples
