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vit-base-patch16-224-ve-U11-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-U11-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: 0.9130434782608695

vit-base-patch16-224-ve-U11-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: 0.4436
  • Accuracy: 0.9130

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 1.3798 0.5435
1.3792 2.0 13 1.3091 0.6522
1.3792 2.92 19 1.2227 0.5870
1.2783 4.0 26 1.1263 0.6087
1.1226 4.92 32 1.0466 0.6522
1.1226 6.0 39 0.9854 0.5870
0.9881 6.92 45 0.9303 0.6957
0.8707 8.0 52 0.8806 0.7826
0.8707 8.92 58 0.8234 0.7826
0.7604 10.0 65 0.7159 0.8261
0.6452 10.92 71 0.6929 0.8478
0.6452 12.0 78 0.6491 0.8696
0.5576 12.92 84 0.5924 0.8478
0.4708 14.0 91 0.5551 0.8478
0.4708 14.92 97 0.6354 0.8043
0.422 16.0 104 0.5130 0.8696
0.3546 16.92 110 0.5302 0.8696
0.3546 18.0 117 0.4436 0.9130
0.3353 18.92 123 0.5621 0.8261
0.3106 20.0 130 0.4912 0.8696
0.3106 20.92 136 0.4747 0.8913
0.312 22.0 143 0.4603 0.8913
0.312 22.15 144 0.4598 0.8913

Framework versions

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