Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use pradanaadn/vit-emotional-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pradanaadn/vit-emotional-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pradanaadn/vit-emotional-classifier") 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("pradanaadn/vit-emotional-classifier") model = AutoModelForImageClassification.from_pretrained("pradanaadn/vit-emotional-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d2ace682c573092b8f559ddeae14e97a7ad16bcacb40a06d714f2c9001091b4
- Size of remote file:
- 5.11 kB
- SHA256:
- 9028f08307043947b0267ebecf5cd3e9ee1e01463a6e7d7c7ee791b5971d6888
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