Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use ricardoSLabs/Fer_vit_jaffe_crop_GOOGLE_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricardoSLabs/Fer_vit_jaffe_crop_GOOGLE_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ricardoSLabs/Fer_vit_jaffe_crop_GOOGLE_1") 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("ricardoSLabs/Fer_vit_jaffe_crop_GOOGLE_1") model = AutoModelForImageClassification.from_pretrained("ricardoSLabs/Fer_vit_jaffe_crop_GOOGLE_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 354f92413d8452274d7ed236a7d9814847681944384ca0d6f69584681b9de64b
- Size of remote file:
- 5.24 kB
- SHA256:
- ada7ba9b1b18a32c9840685b01cd16a1bed5181b34d64138777f3cce8bd11434
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