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 model.safetensors from prithivMLmods/Human-Action-Recognition: direct link, hf CLI and curl.
- Browser
- Download file 372 MB
-
https://huggingface.co/prithivMLmods/Human-Action-Recognition/resolve/main/model.safetensors
- Command line
-
hf download hf://prithivMLmods/Human-Action-Recognition/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/prithivMLmods/Human-Action-Recognition/resolve/main/model.safetensors
372 MB
- Xet hash:
- a6df5aa16fe564b609d5390296e1bf6dcbc01977869def6be39fdedb659031a3
- Size of remote file:
- 372 MB
- SHA256:
- 8e2b9d5be40e7e84d93cbe40bf0b2b67d36503aa45faab3593811c712c53f649
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.