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metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: deit-tiny-patch16-224-finetuned-piid
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: val
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7625570776255708

deit-tiny-patch16-224-finetuned-piid

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

  • Loss: 0.5426
  • Accuracy: 0.7626

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.2274 0.98 20 1.1185 0.4658
0.8485 2.0 41 0.8690 0.6119
0.6793 2.98 61 0.8749 0.6073
0.6028 4.0 82 0.6864 0.6804
0.5693 4.98 102 0.5618 0.7717
0.5092 6.0 123 0.5958 0.7260
0.3788 6.98 143 0.6444 0.7352
0.4106 8.0 164 0.5277 0.7443
0.3716 8.98 184 0.6081 0.7352
0.3466 10.0 205 0.4976 0.7580
0.3587 10.98 225 0.5429 0.7443
0.2661 12.0 246 0.4933 0.7763
0.2628 12.98 266 0.5078 0.7671
0.2473 14.0 287 0.5264 0.7945
0.2633 14.98 307 0.5262 0.7671
0.2017 16.0 328 0.5509 0.7763
0.1861 16.98 348 0.5513 0.7443
0.2031 18.0 369 0.5516 0.7580
0.1604 18.98 389 0.5430 0.7671
0.2346 19.51 400 0.5426 0.7626

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1