Instructions to use agriggio/smart-onnx-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use agriggio/smart-onnx-models with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("agriggio/smart-onnx-models") 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("agriggio/smart-onnx-models") 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
Yesterday
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Add raw denoising models (greycard denoise, RawForge TreeNet)
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README: denoising models, licenses
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Model card: remove the RawNIND section
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Remove the RawNIND models
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Add RawNIND denoisers (Bayer and linear) and their export tool
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2 days ago
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Link SMART and ART in the model card
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Add SAM2.1 ONNX models and CoreML-ready LaMa
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initial commit
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