Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use grhaputra/emotion_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grhaputra/emotion_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="grhaputra/emotion_image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("grhaputra/emotion_image_classification") model = AutoModelForImageClassification.from_pretrained("grhaputra/emotion_image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from grhaputra/emotion_image_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/grhaputra/emotion_image_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://grhaputra/emotion_image_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/grhaputra/emotion_image_classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 68f4f9606058500eabae45dcdf3c53d86460e57c0da7c14c107c62136d7e4665
- Size of remote file:
- 4.6 kB
- SHA256:
- 9183aaa5722f62d8ab8ddb7a7b820e54cc8546bd62165e6fdc62706553159445
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