Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Marxulia/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marxulia/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Marxulia/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("Marxulia/emotion_classification") model = AutoModelForImageClassification.from_pretrained("Marxulia/emotion_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01b394c33f02f8216f4a3759edd36f070c35f12dca4721f18e3e5f8709797ed2
- Size of remote file:
- 343 MB
- SHA256:
- b97869f41595064200eebfe8396319e4b59e3e54024a2b1b3130e3ed455b224b
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