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Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold1

Image Classification
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swin
generated_from_trainer
Eval Results
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metadata
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_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: 0.6550881953867028

Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold1

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3289
  • Accuracy: 0.6551

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
1.1406 1.0 924 1.1487 0.6011
0.8906 2.0 1848 1.0575 0.6472
0.9616 3.0 2772 0.9518 0.6657
0.6331 4.0 3696 1.0209 0.6611
0.6302 5.0 4620 1.0093 0.6749
0.5342 6.0 5544 1.1249 0.6608
0.408 7.0 6468 1.1207 0.6676
0.2471 8.0 7392 1.2523 0.6649
0.2109 9.0 8316 1.3584 0.6583
0.1506 10.0 9240 1.5006 0.6502
0.1883 11.0 10164 1.5942 0.6545
0.1463 12.0 11088 1.7342 0.6488
0.1095 13.0 12012 1.8730 0.6526
0.0854 14.0 12936 1.9291 0.6526
0.0397 15.0 13860 2.0830 0.6559
0.0432 16.0 14784 2.1495 0.6540
0.0261 17.0 15708 2.2222 0.6488
0.0435 18.0 16632 2.2862 0.6556
0.0983 19.0 17556 2.3223 0.6551
0.0738 20.0 18480 2.3289 0.6551

Framework versions

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