Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use digo-prayudha/vit-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digo-prayudha/vit-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="digo-prayudha/vit-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("digo-prayudha/vit-emotion-classification") model = AutoModelForImageClassification.from_pretrained("digo-prayudha/vit-emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 533c66c8437c9a8e41412734058a7903fc82f2ce30407367463847c915601c22
- Size of remote file:
- 5.3 kB
- SHA256:
- e4e554804ca69ba63c5dfc5d687264a589bf43c750133db4f89df8bdafce4473
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