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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_fold5
    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: 0.5611608353675075

Boya1_Adamax_1-e4_20Epoch_Deit-tiny-patch16_fold5

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: 3.4928
  • Accuracy: 0.5612

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
1.3998 1.0 924 1.5149 0.4893
1.3931 2.0 1848 1.3087 0.5424
1.0339 3.0 2772 1.2795 0.5598
0.6895 4.0 3696 1.2909 0.5772
0.5865 5.0 4620 1.4017 0.5644
0.6353 6.0 5544 1.5191 0.5606
0.4014 7.0 6468 1.6877 0.5644
0.2889 8.0 7392 1.9030 0.5601
0.064 9.0 8316 2.0982 0.5633
0.0342 10.0 9240 2.4199 0.5574
0.071 11.0 10164 2.6790 0.5555
0.0036 12.0 11088 2.8221 0.5571
0.0018 13.0 12012 3.0343 0.5609
0.0019 14.0 12936 3.1489 0.5566
0.0004 15.0 13860 3.2320 0.5579
0.0248 16.0 14784 3.2970 0.5595
0.0003 17.0 15708 3.3641 0.5625
0.0002 18.0 16632 3.4302 0.5606
0.0002 19.0 17556 3.4763 0.5620
0.0001 20.0 18480 3.4928 0.5612

Framework versions

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