Instructions to use usyd-community/vitpose-base-coco-aic-mpii with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usyd-community/vitpose-base-coco-aic-mpii with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, VitPoseForPoseEstimation processor = AutoImageProcessor.from_pretrained("usyd-community/vitpose-base-coco-aic-mpii") model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-coco-aic-mpii", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from usyd-community/vitpose-base-coco-aic-mpii: direct link, hf CLI and curl.
- Browser
- Download file 360 MB
-
https://huggingface.co/usyd-community/vitpose-base-coco-aic-mpii/resolve/main/model.safetensors
- Command line
-
hf download hf://usyd-community/vitpose-base-coco-aic-mpii/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/usyd-community/vitpose-base-coco-aic-mpii/resolve/main/model.safetensors
360 MB
- Xet hash:
- ac3ee45fe2d837b6e4c6dff8a0eaf18d96cb04e0115ffc41b68799bd289412a3
- Size of remote file:
- 360 MB
- SHA256:
- 5620aff72b72d229f52596d497edd42b0bebeec31b4001285db8fe8d4ab59ef5
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