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smids_5x_deit_tiny_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-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_tiny_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: 0.7762938230383973

smids_5x_deit_tiny_sgd_0001_fold1

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

  • Loss: 0.5289
  • Accuracy: 0.7763

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
1.163 1.0 376 1.1394 0.3756
1.1206 2.0 752 1.0830 0.4240
1.0283 3.0 1128 1.0398 0.4624
1.0304 4.0 1504 1.0025 0.4925
0.9327 5.0 1880 0.9675 0.5159
0.9025 6.0 2256 0.9343 0.5342
0.813 7.0 2632 0.9040 0.5492
0.8373 8.0 3008 0.8750 0.5793
0.8156 9.0 3384 0.8442 0.6093
0.7741 10.0 3760 0.8156 0.6344
0.6787 11.0 4136 0.7888 0.6477
0.6914 12.0 4512 0.7651 0.6561
0.6673 13.0 4888 0.7424 0.6761
0.6471 14.0 5264 0.7219 0.6895
0.5827 15.0 5640 0.7043 0.6978
0.5679 16.0 6016 0.6891 0.7062
0.5387 17.0 6392 0.6730 0.7095
0.5827 18.0 6768 0.6602 0.7145
0.5764 19.0 7144 0.6481 0.7179
0.5667 20.0 7520 0.6375 0.7312
0.5598 21.0 7896 0.6271 0.7329
0.4963 22.0 8272 0.6181 0.7346
0.5399 23.0 8648 0.6097 0.7396
0.6005 24.0 9024 0.6025 0.7429
0.5535 25.0 9400 0.5952 0.7479
0.5292 26.0 9776 0.5886 0.7513
0.4834 27.0 10152 0.5826 0.7529
0.4735 28.0 10528 0.5772 0.7546
0.5034 29.0 10904 0.5722 0.7563
0.4846 30.0 11280 0.5675 0.7579
0.5398 31.0 11656 0.5634 0.7596
0.5623 32.0 12032 0.5594 0.7646
0.5122 33.0 12408 0.5557 0.7646
0.483 34.0 12784 0.5523 0.7663
0.4676 35.0 13160 0.5492 0.7663
0.4655 36.0 13536 0.5464 0.7679
0.4231 37.0 13912 0.5438 0.7663
0.5103 38.0 14288 0.5415 0.7663
0.4626 39.0 14664 0.5394 0.7679
0.4372 40.0 15040 0.5375 0.7679
0.496 41.0 15416 0.5357 0.7713
0.4002 42.0 15792 0.5343 0.7746
0.4506 43.0 16168 0.5329 0.7746
0.472 44.0 16544 0.5318 0.7746
0.467 45.0 16920 0.5309 0.7763
0.4861 46.0 17296 0.5301 0.7746
0.4576 47.0 17672 0.5296 0.7746
0.4597 48.0 18048 0.5292 0.7763
0.4465 49.0 18424 0.5290 0.7763
0.3972 50.0 18800 0.5289 0.7763

Framework versions

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