Mask Generation
ONNX
sam2
segment-anything
sam-med3d
microscopy
medical-imaging
interactive-segmentation
feature-extraction
pixel-classification
dinov2
vgg16
cellpose
micro-sam
Instructions to use voxide/voxide-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use voxide/voxide-models with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("voxide/voxide-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("voxide/voxide-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
Download sam2.1_hiera_base_plus_decoder.onnx from voxide/voxide-models: direct link, hf CLI and curl.
- Browser
- Download file 16.5 MB
-
https://huggingface.co/voxide/voxide-models/resolve/main/sam2.1_hiera_base_plus_decoder.onnx
- Command line
-
hf download hf://voxide/voxide-models/sam2.1_hiera_base_plus_decoder.onnx
-
curl -L -o sam2.1_hiera_base_plus_decoder.onnx https://huggingface.co/voxide/voxide-models/resolve/main/sam2.1_hiera_base_plus_decoder.onnx
16.5 MB
- Xet hash:
- 51beae8a02223b82ebeaf62e8b8a7b8ae55eb4071467673f2f4c13b332f7704c
- Size of remote file:
- 16.5 MB
- SHA256:
- 8415257a6befe697151007df917e2fcb79c3c940a643bc664b5e1c8bc67d58df
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