Instructions to use Kushagra07/autotrain-vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushagra07/autotrain-vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kushagra07/autotrain-vit-base-patch16-224") 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("Kushagra07/autotrain-vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("Kushagra07/autotrain-vit-base-patch16-224") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metrics
loss: 0.223019078373909
f1_macro: 0.8656901233453752
f1_micro: 0.9238648002731308
f1_weighted: 0.9239248911195606
precision_macro: 0.928029530990028
precision_micro: 0.9238648002731308
precision_weighted: 0.9287629201629745
recall_macro: 0.834713663096659
recall_micro: 0.9238648002731308
recall_weighted: 0.9238648002731308
accuracy: 0.9238648002731308
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