Image Segmentation
sam2
Safetensors
segmentation
medical-imaging
polyp-detection
gastrointestinal
colonoscopy
Eval Results (legacy)
Instructions to use usama10/sam2-kvasir-polyp-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use usama10/sam2-kvasir-polyp-segmentation with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(usama10/sam2-kvasir-polyp-segmentation) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(usama10/sam2-kvasir-polyp-segmentation) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- e25f2a446958421f20e000ea95980a28c7884dd28e3b1e55898a44c307007f64
- Size of remote file:
- 167 kB
- SHA256:
- 12e5b37cbb02383bb756dd29f28d6a2d37c7274ca378c1385db6b6dd2ed5e336
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.