README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - mnist
  - autoevaluate/mnist-sample
metrics:
  - accuracy
model-index:
  - name: image-classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: mnist
          type: mnist
          args: mnist
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9833333333333333

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