Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use malifiahm/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malifiahm/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="malifiahm/emotion_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("malifiahm/emotion_classification") model = AutoModelForImageClassification.from_pretrained("malifiahm/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from malifiahm/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/malifiahm/emotion_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://malifiahm/emotion_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/malifiahm/emotion_classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 5b5555b6e86511e0254dfb88c6e863d8a901dda669b0111cfce8ba23d8eb2715
- Size of remote file:
- 4.6 kB
- SHA256:
- aa46825428f057fc4207aca962742cf091cfdc91ed61a1fd742696ae1d84f2f3
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