Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jayanta/google-vit-base-patch16-224-cartoon-emotion-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayanta/google-vit-base-patch16-224-cartoon-emotion-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jayanta/google-vit-base-patch16-224-cartoon-emotion-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("jayanta/google-vit-base-patch16-224-cartoon-emotion-detection") model = AutoModelForImageClassification.from_pretrained("jayanta/google-vit-base-patch16-224-cartoon-emotion-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from jayanta/google-vit-base-patch16-224-cartoon-emotion-detection: direct link, hf CLI and curl.
- Browser
- Download file 5.23 kB
-
https://huggingface.co/jayanta/google-vit-base-patch16-224-cartoon-emotion-detection/resolve/main/trainer_state.json
- Command line
-
hf download hf://jayanta/google-vit-base-patch16-224-cartoon-emotion-detection/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/jayanta/google-vit-base-patch16-224-cartoon-emotion-detection/resolve/main/trainer_state.json
5.23 kB
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