Instructions to use prithivMLmods/Human-Action-Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Human-Action-Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Human-Action-Recognition") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Human-Action-Recognition") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Human-Action-Recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-394/optimizer.pt from prithivMLmods/Human-Action-Recognition: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Human-Action-Recognition/resolve/main/checkpoint-394/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Human-Action-Recognition/checkpoint-394/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Human-Action-Recognition/resolve/main/checkpoint-394/optimizer.pt
687 MB
- Xet hash:
- 89487309bb64c5ec86c07856a3185f7f12d1fa65113814787c665ab4ab2cca7c
- Size of remote file:
- 687 MB
- SHA256:
- 8bd85ca6992cfc002f42375a1f2772a60bc0c2dbbab23b2936bd8b7a8fbfb2af
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