Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use kazuma313/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kazuma313/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kazuma313/emotion_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("kazuma313/emotion_classification") model = AutoModelForImageClassification.from_pretrained("kazuma313/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12fe95b5df2aaf9b3a76e80df1f97d653438afcc197f28c005c2f0fe772ea202
- Size of remote file:
- 343 MB
- SHA256:
- f42ec946cc0481cd3022c43814e375329d6d30af0278f0f194875eaba6576181
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