Instructions to use square-zero-labs/sam2.1-tiny-video-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use square-zero-labs/sam2.1-tiny-video-onnx with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(square-zero-labs/sam2.1-tiny-video-onnx) 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(square-zero-labs/sam2.1-tiny-video-onnx) 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
SAM2.1-tiny full video tracking pipeline (validated ONNX export, worst frame IoU 0.9967 vs PyTorch reference)
a4144e8 verified - Xet hash:
- 5a18b377382a1a44e15621460cae9a71b521160ca1b00a3c3c585b8420893a0c
- Size of remote file:
- 5.62 MB
- SHA256:
- 580d246c109de88838f600ba7c1c0d03d1fe267f7641b06f8c88c5f0dc5834cd
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