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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: Boya1_Adamax_1-e4_20Epoch_Deit-tiny-patch16_fold1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5568829758349172

Boya1_Adamax_1-e4_20Epoch_Deit-tiny-patch16_fold1

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: 3.5356
  • Accuracy: 0.5569

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • 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
1.3865 1.0 924 1.5601 0.4768
1.2182 2.0 1848 1.4517 0.4963
1.2424 3.0 2772 1.3040 0.5531
0.8291 4.0 3696 1.3092 0.5745
0.6764 5.0 4620 1.3977 0.5724
0.5779 6.0 5544 1.5087 0.5601
0.3166 7.0 6468 1.7036 0.5577
0.2404 8.0 7392 1.9068 0.5528
0.144 9.0 8316 2.1442 0.5547
0.165 10.0 9240 2.4839 0.5509
0.0646 11.0 10164 2.7042 0.5490
0.0029 12.0 11088 2.9034 0.5523
0.0317 13.0 12012 3.1091 0.5504
0.0012 14.0 12936 3.2476 0.5496
0.0008 15.0 13860 3.3162 0.5569
0.0005 16.0 14784 3.3879 0.5525
0.0003 17.0 15708 3.4370 0.5517
0.0007 18.0 16632 3.4907 0.5542
0.0003 19.0 17556 3.5171 0.5566
0.0003 20.0 18480 3.5356 0.5569

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.1.0
  • Datasets 2.19.0
  • Tokenizers 0.19.1