Instructions to use dima806/yoga_pose_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/yoga_pose_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/yoga_pose_image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/yoga_pose_image_classification") model = AutoModelForImageClassification.from_pretrained("dima806/yoga_pose_image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-290/optimizer.pt from dima806/yoga_pose_image_classification: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/dima806/yoga_pose_image_classification/resolve/main/checkpoint-290/optimizer.pt
- Command line
-
hf download hf://dima806/yoga_pose_image_classification/checkpoint-290/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dima806/yoga_pose_image_classification/resolve/main/checkpoint-290/optimizer.pt
687 MB
- Xet hash:
- 16cc7cccb7ab0734b311a6b1793182d717ee5a646ea9176efca289f4ba9ea87e
- Size of remote file:
- 687 MB
- SHA256:
- 6e5c4f21c00457104418ccedcb96c3b94c6e2fa0a1b8c3d7cc1048b5a1b533e3
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