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image-multi-class-classification-not-evaluated

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image-multi-class-classification-not-evaluated / README.md
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lewtun HF staff
Add evaluation results on the autoevaluate--mnist-sample config and test split of autoevaluate/mnist-sample
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - mnist
  - autoevaluate/mnist-sample
metrics:
  - accuracy
duplicated_from: autoevaluate/image-multi-class-classification
model-index:
  - name: autoevaluate/image-multi-class-classification-not-evaluated
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: autoevaluate/mnist-sample
          type: autoevaluate/mnist-sample
          config: autoevaluate--mnist-sample
          split: test
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          - type: precision
            value: 0.9478535353535353
            name: Precision Macro
            verified: true
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          - type: precision
            value: 0.95
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          - type: precision
            value: 0.9510353535353535
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          - type: recall
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          - type: recall
            value: 0.95
            name: Recall Micro
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          - type: recall
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            verified: true
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image-classification

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the mnist dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0556
  • Accuracy: 0.9833

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3743 1.0 422 0.0556 0.9833

Framework versions

  • Transformers 4.20.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1