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smids_5x_deit_small_sgd_0001_fold1

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_sgd_0001_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: 0.7913188647746243

smids_5x_deit_small_sgd_0001_fold1

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5286
  • Accuracy: 0.7913

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • 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: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0382 1.0 376 1.0714 0.4090
1.0394 2.0 752 1.0356 0.4491
0.9541 3.0 1128 1.0000 0.5042
0.928 4.0 1504 0.9637 0.5559
0.8813 5.0 1880 0.9285 0.5943
0.8344 6.0 2256 0.8967 0.6227
0.7873 7.0 2632 0.8681 0.6361
0.8127 8.0 3008 0.8414 0.6544
0.8077 9.0 3384 0.8166 0.6628
0.7559 10.0 3760 0.7932 0.6811
0.7293 11.0 4136 0.7713 0.6962
0.7184 12.0 4512 0.7510 0.7028
0.6866 13.0 4888 0.7320 0.7078
0.6588 14.0 5264 0.7145 0.7195
0.6425 15.0 5640 0.6984 0.7245
0.6378 16.0 6016 0.6836 0.7312
0.5876 17.0 6392 0.6699 0.7396
0.6379 18.0 6768 0.6573 0.7429
0.6063 19.0 7144 0.6456 0.7479
0.5557 20.0 7520 0.6350 0.7496
0.5709 21.0 7896 0.6253 0.7513
0.5404 22.0 8272 0.6166 0.7563
0.5599 23.0 8648 0.6082 0.7529
0.5567 24.0 9024 0.6008 0.7613
0.5445 25.0 9400 0.5938 0.7646
0.5273 26.0 9776 0.5874 0.7629
0.5187 27.0 10152 0.5814 0.7613
0.4686 28.0 10528 0.5760 0.7629
0.502 29.0 10904 0.5710 0.7629
0.5086 30.0 11280 0.5663 0.7663
0.5383 31.0 11656 0.5621 0.7679
0.5306 32.0 12032 0.5581 0.7696
0.4719 33.0 12408 0.5545 0.7713
0.4733 34.0 12784 0.5512 0.7763
0.4916 35.0 13160 0.5482 0.7796
0.4659 36.0 13536 0.5454 0.7796
0.4447 37.0 13912 0.5429 0.7830
0.5196 38.0 14288 0.5406 0.7830
0.4685 39.0 14664 0.5386 0.7830
0.4526 40.0 15040 0.5367 0.7830
0.4896 41.0 15416 0.5350 0.7863
0.4446 42.0 15792 0.5336 0.7863
0.4328 43.0 16168 0.5323 0.7863
0.5156 44.0 16544 0.5312 0.7880
0.4252 45.0 16920 0.5303 0.7896
0.4576 46.0 17296 0.5296 0.7896
0.4261 47.0 17672 0.5291 0.7913
0.4841 48.0 18048 0.5288 0.7913
0.4563 49.0 18424 0.5286 0.7913
0.4361 50.0 18800 0.5286 0.7913

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2