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Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold5

Image Classification
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Safetensors
swin
generated_from_trainer
Eval Results
Inference Endpoints
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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_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: 0.6725399837354297

Boya1_RMSProp_1-e5_20Epoch_swin-base-window7-224-in22k_fold5

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: 2.4142
  • Accuracy: 0.6725

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
1.1865 1.0 924 1.1272 0.6132
0.9864 2.0 1848 1.0047 0.6555
0.7613 3.0 2772 0.9529 0.6720
0.5344 4.0 3696 0.9895 0.6780
0.5033 5.0 4620 1.0342 0.6685
0.5228 6.0 5544 1.0876 0.6709
0.4147 7.0 6468 1.1734 0.6690
0.2373 8.0 7392 1.3076 0.6617
0.2292 9.0 8316 1.4535 0.6579
0.1735 10.0 9240 1.5622 0.6625
0.1961 11.0 10164 1.6717 0.6663
0.1611 12.0 11088 1.8090 0.6706
0.0814 13.0 12012 1.9522 0.6633
0.0487 14.0 12936 2.0777 0.6649
0.0762 15.0 13860 2.1837 0.6628
0.1072 16.0 14784 2.2581 0.6663
0.0286 17.0 15708 2.3433 0.6671
0.0537 18.0 16632 2.3849 0.6679
0.0348 19.0 17556 2.4070 0.6739
0.0662 20.0 18480 2.4142 0.6725

Framework versions

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