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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: 0.8127853881278538

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: 0.5154
  • Accuracy: 0.8128

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
1.2298 0.98 20 1.1138 0.4703
0.7642 2.0 41 0.9123 0.5982
0.6167 2.98 61 0.6736 0.6849
0.5628 4.0 82 0.6615 0.7032
0.5616 4.98 102 0.5985 0.7352
0.4742 6.0 123 0.4981 0.7854
0.3434 6.98 143 0.5729 0.7489
0.3691 8.0 164 0.5779 0.7397
0.3375 8.98 184 0.5417 0.7580
0.3192 10.0 205 0.5554 0.7534
0.2795 10.98 225 0.5656 0.7763
0.242 12.0 246 0.5319 0.7991
0.2557 12.98 266 0.5154 0.8128
0.2465 14.0 287 0.5763 0.7991
0.221 14.98 307 0.5683 0.8037
0.2058 16.0 328 0.5852 0.8128
0.1809 16.98 348 0.6282 0.8082
0.1638 18.0 369 0.6289 0.7945
0.155 18.98 389 0.6134 0.8037
0.2094 19.51 400 0.6114 0.8082

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3