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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_3Class_RMSprop_1-e5_20Epoch_Beit-base-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: 0.8310279359913209

Boya1_3Class_RMSprop_1-e5_20Epoch_Beit-base-patch16_fold5

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: 1.7877
  • Accuracy: 0.8310

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: 1e-05
  • 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
0.5804 1.0 924 0.4986 0.8004
0.4299 2.0 1848 0.4370 0.8248
0.2235 3.0 2772 0.4410 0.8446
0.1347 4.0 3696 0.5720 0.8343
0.0488 5.0 4620 0.8207 0.8275
0.2009 6.0 5544 1.0317 0.8329
0.0566 7.0 6468 1.3823 0.8205
0.0733 8.0 7392 1.3466 0.8324
0.0357 9.0 8316 1.3267 0.8362
0.071 10.0 9240 1.5459 0.8264
0.0505 11.0 10164 1.6231 0.8280
0.1165 12.0 11088 1.6016 0.8297
0.0243 13.0 12012 1.7023 0.8351
0.0327 14.0 12936 1.6673 0.8354
0.002 15.0 13860 1.7768 0.8259
0.0008 16.0 14784 1.8057 0.8302
0.0117 17.0 15708 1.8092 0.8253
0.024 18.0 16632 1.7701 0.8324
0.0349 19.0 17556 1.7881 0.8291
0.0001 20.0 18480 1.7877 0.8310

Framework versions

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