Instructions to use hansin91/activity_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hansin91/activity_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hansin91/activity_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("hansin91/activity_classification") model = AutoModelForImageClassification.from_pretrained("hansin91/activity_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hansin91/activity_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/hansin91/activity_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://hansin91/activity_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hansin91/activity_classification/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 6e6193a97991521a8a9390a883f4a6a9f18b7276b5a7ed4f6387aaa7b2fbd253
- Size of remote file:
- 4.03 kB
- SHA256:
- d023c767e64b7f08a20137745d0e6408fa25baa6dcb040edfa7cf9efddaba8a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.