Instructions to use g-ronimo/sam2-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use g-ronimo/sam2-tiny with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(g-ronimo/sam2-tiny) 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(g-ronimo/sam2-tiny) 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:
- 30e66b9f59ef60b0efaa6e7c9ed0f54324d4ea38818b0f8b5377d21924ad7199
- Size of remote file:
- 16.5 MB
- SHA256:
- 4a4c8a8a53d9722834b66d8ced88770e2cc783e367965ba62a13be3ab5f1ba89
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