Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data") 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("Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data") model = AutoModelForImageClassification.from_pretrained("Soulaimen/swin-tiny-patch4-window7-224-bottom_cleaned_data") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.9726
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## Model description
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0839
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- Accuracy: 0.9726
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## Model description
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