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vit-base-patch16-224-ve-U13-b-24

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-ve-U13-b-24
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8478260869565217

vit-base-patch16-224-ve-U13-b-24

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

  • Loss: 0.5896
  • Accuracy: 0.8478

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: 5.5e-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: 0.05
  • num_epochs: 24

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.92 6 1.3800 0.4565
1.3792 2.0 13 1.3093 0.5870
1.3792 2.92 19 1.2228 0.5
1.2786 4.0 26 1.1303 0.5652
1.1265 4.92 32 1.0615 0.5435
1.1265 6.0 39 1.0205 0.4565
0.9906 6.92 45 0.9259 0.6304
0.8632 8.0 52 0.8739 0.7391
0.8632 8.92 58 0.8381 0.7609
0.7529 10.0 65 0.7604 0.7826
0.6468 10.92 71 0.7212 0.8043
0.6468 12.0 78 0.6825 0.7826
0.5553 12.92 84 0.6409 0.8261
0.4704 14.0 91 0.6471 0.8261
0.4704 14.92 97 0.6296 0.7609
0.415 16.0 104 0.5896 0.8478
0.3444 16.92 110 0.5828 0.8043
0.3444 18.0 117 0.5771 0.8261
0.3212 18.92 123 0.5672 0.8261
0.3021 20.0 130 0.5596 0.8478
0.3021 20.92 136 0.5527 0.8261
0.3004 22.0 143 0.5429 0.8261
0.3004 22.15 144 0.5427 0.8261

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0