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
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
Browse files- onnx/vision_encoder.onnx +2 -2
onnx/vision_encoder.onnx
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