README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - mnist
metrics:
  - accuracy
model-index:
  - name: vit-base-mnist
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: mnist
          type: mnist
          config: mnist
          split: train
          args: mnist
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9948888888888889

vit-base-mnist

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset. It achieves the following results on the evaluation set:

• Loss: 0.0236
• Accuracy: 0.9949

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3717	1.0	6375	0.0522	0.9893
0.3453	2.0	12750	0.0370	0.9906
0.3736	3.0	19125	0.0308	0.9916
0.3224	4.0	25500	0.0269	0.9939
0.2846	5.0	31875	0.0236	0.9949

Framework versions

• Transformers 4.22.0.dev0
• Pytorch 1.11.0a0+17540c5
• Datasets 2.4.0
• Tokenizers 0.12.1