Instructions to use Kushagra07/autotrain-swin-tiny-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushagra07/autotrain-swin-tiny-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kushagra07/autotrain-swin-tiny-patch4-window7-224") 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-swin-tiny-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("Kushagra07/autotrain-swin-tiny-patch4-window7-224") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metrics
loss: 0.3038078248500824
f1_macro: 0.7294036951655769
f1_micro: 0.899283031751451
f1_weighted: 0.8963777407391669
precision_macro: 0.8462013295295603
precision_micro: 0.899283031751451
precision_weighted: 0.9070935900298
recall_macro: 0.6921156764861889
recall_micro: 0.899283031751451
recall_weighted: 0.899283031751451
accuracy: 0.899283031751451
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