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
Commit History
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mask_decoder: replace rank-5 GatherElements with one-hot Mul+ReduceSum selection (GatherElements generates invalid WGSL in ort-web WebGPU EP). Bit-identical outputs verified vs previous decoder.
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vision_encoder: precompute static pos-embed (removes If/Tile constructs that broke ort-web strict shape inference; loads at graphOptimizationLevel 'all' now). Revalidated: worst frame IoU 0.9967
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SAM2.1-tiny full video tracking pipeline (validated ONNX export, worst frame IoU 0.9967 vs PyTorch reference)
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initial commit
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