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HorcruxNo13
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swin-tiny-patch4-window7-224

Image Classification
Transformers
PyTorch
swin
Generated from Trainer
Eval Results
Inference Endpoints
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swin-tiny-patch4-window7-224 / README.md
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metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: swin-tiny-patch4-window7-224
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7433333333333333
          - name: Precision
            type: precision
            value: 0.7306273291925466
          - name: Recall
            type: recall
            value: 0.7433333333333333

swin-tiny-patch4-window7-224

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

  • Loss: 0.5534
  • Accuracy: 0.7433
  • Precision: 0.7306
  • Recall: 0.7433
  • F1 Score: 0.7344

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Score
No log 1.0 4 0.7306 0.4 0.6521 0.4 0.3821
No log 2.0 8 0.5815 0.7333 0.8050 0.7333 0.6286
No log 3.0 12 0.5700 0.725 0.5256 0.725 0.6094
No log 4.0 16 0.5635 0.725 0.5256 0.725 0.6094
No log 5.0 20 0.5509 0.7292 0.8028 0.7292 0.6191
No log 6.0 24 0.5356 0.7417 0.7438 0.7417 0.6589
No log 7.0 28 0.5353 0.75 0.7360 0.75 0.6895
No log 8.0 32 0.5299 0.7375 0.7090 0.7375 0.6668
No log 9.0 36 0.5335 0.7667 0.7509 0.7667 0.7310
No log 10.0 40 0.5344 0.7417 0.7315 0.7417 0.6644
No log 11.0 44 0.5297 0.7458 0.7279 0.7458 0.6821
No log 12.0 48 0.5202 0.75 0.7360 0.75 0.6895
0.5942 13.0 52 0.5325 0.7542 0.7411 0.7542 0.7452
0.5942 14.0 56 0.5139 0.7583 0.7505 0.7583 0.7039
0.5942 15.0 60 0.5528 0.7417 0.7347 0.7417 0.7377
0.5942 16.0 64 0.5070 0.7625 0.7437 0.7625 0.7277
0.5942 17.0 68 0.5193 0.775 0.7594 0.775 0.7592
0.5942 18.0 72 0.5090 0.7583 0.7448 0.7583 0.7487
0.5942 19.0 76 0.5189 0.7792 0.7847 0.7792 0.7816
0.5942 20.0 80 0.5214 0.775 0.7795 0.775 0.7770
0.5942 21.0 84 0.5188 0.775 0.7710 0.775 0.7728
0.5942 22.0 88 0.5029 0.7667 0.7526 0.7667 0.7557
0.5942 23.0 92 0.5061 0.7833 0.7734 0.7833 0.7761
0.5942 24.0 96 0.5350 0.7667 0.7713 0.7667 0.7687
0.4829 25.0 100 0.5149 0.7542 0.7330 0.7542 0.7337
0.4829 26.0 104 0.5283 0.7583 0.7737 0.7583 0.7641
0.4829 27.0 108 0.5109 0.7792 0.7647 0.7792 0.7646
0.4829 28.0 112 0.5258 0.775 0.7729 0.775 0.7739
0.4829 29.0 116 0.5207 0.7625 0.745 0.7625 0.7468
0.4829 30.0 120 0.5306 0.75 0.7357 0.75 0.7400
0.4829 31.0 124 0.5455 0.75 0.7375 0.75 0.7417
0.4829 32.0 128 0.5653 0.7458 0.7380 0.7458 0.7412
0.4829 33.0 132 0.5565 0.7417 0.7212 0.7417 0.7256
0.4829 34.0 136 0.5468 0.7708 0.7658 0.7708 0.7679
0.4829 35.0 140 0.5268 0.7833 0.7723 0.7833 0.7747
0.4829 36.0 144 0.5260 0.775 0.7710 0.775 0.7728
0.4829 37.0 148 0.5281 0.775 0.7659 0.775 0.7689
0.3846 38.0 152 0.5385 0.7708 0.7742 0.7708 0.7724
0.3846 39.0 156 0.5253 0.7708 0.7623 0.7708 0.7653
0.3846 40.0 160 0.5319 0.7708 0.7719 0.7708 0.7714
0.3846 41.0 164 0.5311 0.775 0.7631 0.775 0.7660
0.3846 42.0 168 0.5325 0.7792 0.7683 0.7792 0.7711
0.3846 43.0 172 0.5254 0.7667 0.7606 0.7667 0.7631
0.3846 44.0 176 0.5232 0.7708 0.7623 0.7708 0.7653
0.3846 45.0 180 0.5291 0.7708 0.7640 0.7708 0.7667
0.3846 46.0 184 0.5356 0.7708 0.7607 0.7708 0.7639
0.3846 47.0 188 0.5400 0.7708 0.7607 0.7708 0.7639
0.3846 48.0 192 0.5409 0.7667 0.7540 0.7667 0.7573
0.3846 49.0 196 0.5403 0.7667 0.7540 0.7667 0.7573
0.3353 50.0 200 0.5397 0.7708 0.7592 0.7708 0.7624

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3