test_trainer / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: test_trainer
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.915

test_trainer

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

  • Loss: 0.8643
  • Accuracy: 0.915

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 125 3.6903 0.517
No log 2.0 250 2.7990 0.553
No log 3.0 375 2.3198 0.57
3.1391 4.0 500 2.0210 0.632
3.1391 5.0 625 1.8298 0.638
3.1391 6.0 750 1.6753 0.683
3.1391 7.0 875 1.5446 0.708
1.7309 8.0 1000 1.4338 0.751
1.7309 9.0 1125 1.3318 0.777
1.7309 10.0 1250 1.2387 0.807
1.7309 11.0 1375 1.1828 0.806
1.2855 12.0 1500 1.1052 0.843
1.2855 13.0 1625 1.0620 0.862
1.2855 14.0 1750 1.0029 0.87
1.2855 15.0 1875 0.9611 0.895
1.0212 16.0 2000 0.9314 0.905
1.0212 17.0 2125 0.9041 0.905
1.0212 18.0 2250 0.8840 0.913
1.0212 19.0 2375 0.8730 0.921
0.8953 20.0 2500 0.8639 0.92

Framework versions

  • Transformers 4.37.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1