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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-RU2-40
    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: 0.8333333333333334

vit-base-patch16-224-RU2-40

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: 1.2003
  • Accuracy: 0.8333

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: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3226 0.99 38 1.2293 0.6
0.9048 2.0 77 0.7969 0.7
0.4039 2.99 115 0.6800 0.7167
0.281 4.0 154 0.8892 0.7667
0.1755 4.99 192 0.9072 0.7333
0.1035 6.0 231 0.8036 0.8167
0.1275 6.99 269 0.8627 0.8
0.107 8.0 308 0.8713 0.8
0.0984 8.99 346 0.9660 0.8
0.0823 10.0 385 1.0704 0.7833
0.0771 10.99 423 0.9409 0.7667
0.0527 12.0 462 1.0052 0.7833
0.0708 12.99 500 0.9578 0.8
0.0562 14.0 539 1.0712 0.8167
0.0467 14.99 577 1.0586 0.8167
0.0445 16.0 616 1.2066 0.7667
0.0474 16.99 654 1.1863 0.75
0.0263 18.0 693 1.1207 0.8167
0.0307 18.99 731 1.1813 0.8167
0.0393 20.0 770 1.3761 0.75
0.0475 20.99 808 1.3008 0.7667
0.0215 22.0 847 1.2625 0.7333
0.0311 22.99 885 1.1508 0.8
0.027 24.0 924 1.3035 0.7667
0.0251 24.99 962 1.2270 0.7667
0.0161 26.0 1001 1.1470 0.8167
0.0258 26.99 1039 1.1473 0.8167
0.0142 28.0 1078 1.2326 0.7667
0.0151 28.99 1116 1.3978 0.7667
0.021 30.0 1155 1.2003 0.8333
0.0158 30.99 1193 1.2488 0.7667
0.0163 32.0 1232 1.3232 0.75
0.0143 32.99 1270 1.2467 0.8
0.02 34.0 1309 1.3176 0.7833
0.0128 34.99 1347 1.3083 0.7667
0.0144 36.0 1386 1.3080 0.7667
0.0109 36.99 1424 1.2999 0.8
0.0082 38.0 1463 1.2718 0.8
0.0064 38.99 1501 1.2588 0.7667
0.0097 39.48 1520 1.2597 0.7667

Framework versions

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