Instructions to use AhmadYarAI/EdgeTAM-CoreML-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use AhmadYarAI/EdgeTAM-CoreML-Video with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(AhmadYarAI/EdgeTAM-CoreML-Video) 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(AhmadYarAI/EdgeTAM-CoreML-Video) 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
EdgeTAM-CoreML-Video / models /EdgeTAMVideoImageEncoder.mlpackage /Data /com.apple.CoreML /weights /weight.bin
- Xet hash:
- fe5c8b6ea515122a58795a517694c28385edc75caaee1d20b11ba7044041a43e
- Size of remote file:
- 9.82 MB
- SHA256:
- f7ada643c1a6e6620c0aeb4721c7b9359f2bee46c3ef9b96aebae91e86c15d23
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