Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
mnist
handwritten-digits
Generated from Trainer
Instructions to use Fadri/mnist-digit-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fadri/mnist-digit-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Fadri/mnist-digit-recognition") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Fadri/mnist-digit-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [scratch](https://huggingface.co/scratch) on the mnist 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: 0.9944
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [scratch](https://huggingface.co/scratch) on the mnist dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0181
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- Accuracy: 0.9944
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.0687 | 1.0 | 422 | 0.0502 | 0.9857 |
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| 0.0455 | 2.0 | 844 | 0.0347 | 0.989 |
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| 0.0391 | 3.0 | 1266 | 0.0295 | 0.9907 |
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| 0.0299 | 4.0 | 1688 | 0.0281 | 0.9918 |
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| 0.0242 | 5.0 | 2110 | 0.0264 | 0.9915 |
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### Framework versions
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model.safetensors
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training_args.bin
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