Instructions to use 3una/finetuned-AffectNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3una/finetuned-AffectNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="3una/finetuned-AffectNet") 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("3una/finetuned-AffectNet") model = AutoModelForImageClassification.from_pretrained("3una/finetuned-AffectNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from 3una/finetuned-AffectNet: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/3una/finetuned-AffectNet/resolve/main/training_args.bin
- Command line
-
hf download hf://3una/finetuned-AffectNet/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/3una/finetuned-AffectNet/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 82ce7da7a8d54fcedd94f0fb34605b8ba7fdaafdc91a133815a5864addb272b2
- Size of remote file:
- 4.6 kB
- SHA256:
- 08b2c4e210f14da414dda4c46f7c836ce58cd59496d9e36d155ae6d49c10fe2a
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