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metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Boya1_SGD_1-e3_20Epoch_09Momentum_Beit-base-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: 0.4165082812924247

Boya1_SGD_1-e3_20Epoch_09Momentum_Beit-base-patch16_fold1

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8239
  • Accuracy: 0.4165

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: 0.001
  • 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
2.5085 1.0 924 2.4731 0.1963
2.3692 2.0 1848 2.3352 0.2465
2.3278 3.0 2772 2.2372 0.2780
2.1219 4.0 3696 2.1632 0.3044
2.2732 5.0 4620 2.1014 0.3342
2.0973 6.0 5544 2.0509 0.3511
2.0974 7.0 6468 2.0095 0.3633
2.0888 8.0 7392 1.9760 0.3698
1.9477 9.0 8316 1.9428 0.3842
1.937 10.0 9240 1.9178 0.3932
1.9658 11.0 10164 1.8968 0.3932
1.9052 12.0 11088 1.8809 0.3975
1.7933 13.0 12012 1.8676 0.4032
1.9046 14.0 12936 1.8552 0.4062
1.8301 15.0 13860 1.8450 0.4075
1.8479 16.0 14784 1.8378 0.4122
1.8401 17.0 15708 1.8313 0.4138
1.7985 18.0 16632 1.8281 0.4154
1.8691 19.0 17556 1.8245 0.4181
1.8762 20.0 18480 1.8239 0.4165

Framework versions

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