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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bf70d4cc16fc6e76f7d9d8487b251202d8fbeb418d9270c997fee54273e73ba
- Size of remote file:
- 4.6 kB
- SHA256:
- ffc418018e8b70fa2e197fe8292f475f8d015cc1896777e45e5cf6c2c966cdf2
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