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smids_5x_deit_small_rms_001_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_rms_001_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: 0.7662771285475793

smids_5x_deit_small_rms_001_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: 0.5438
  • Accuracy: 0.7663

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: 0.001
  • 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
0.9114 1.0 376 0.8706 0.5559
0.8509 2.0 752 1.2414 0.3456
0.8099 3.0 1128 1.0576 0.4007
0.8085 4.0 1504 0.8246 0.5442
0.8886 5.0 1880 0.8245 0.5376
0.7819 6.0 2256 0.7875 0.5977
0.7498 7.0 2632 0.8002 0.6344
0.7083 8.0 3008 0.8113 0.6027
0.7609 9.0 3384 0.7440 0.6594
0.7953 10.0 3760 0.7639 0.5993
0.694 11.0 4136 0.7065 0.6594
0.7315 12.0 4512 0.7188 0.6277
0.7192 13.0 4888 0.6863 0.7229
0.6504 14.0 5264 0.6661 0.6828
0.6524 15.0 5640 0.6777 0.6661
0.5701 16.0 6016 0.7272 0.6561
0.5543 17.0 6392 0.7125 0.6878
0.6439 18.0 6768 0.6430 0.7028
0.648 19.0 7144 0.6863 0.6928
0.5899 20.0 7520 0.6226 0.7162
0.6393 21.0 7896 0.6018 0.7312
0.5884 22.0 8272 0.5610 0.7412
0.5288 23.0 8648 0.5975 0.7379
0.5965 24.0 9024 0.6473 0.7028
0.58 25.0 9400 0.5765 0.7396
0.5899 26.0 9776 0.6331 0.7245
0.5507 27.0 10152 0.5858 0.7396
0.5002 28.0 10528 0.5674 0.7396
0.5229 29.0 10904 0.5711 0.7629
0.5096 30.0 11280 0.5570 0.7312
0.5311 31.0 11656 0.5601 0.7396
0.5742 32.0 12032 0.6065 0.7346
0.4585 33.0 12408 0.5565 0.7462
0.5294 34.0 12784 0.5555 0.7446
0.5171 35.0 13160 0.5723 0.7462
0.4899 36.0 13536 0.5748 0.7279
0.4582 37.0 13912 0.5789 0.7396
0.5149 38.0 14288 0.5146 0.7679
0.4968 39.0 14664 0.6020 0.7613
0.5645 40.0 15040 0.5459 0.7546
0.4741 41.0 15416 0.5562 0.7479
0.4423 42.0 15792 0.5487 0.7412
0.4186 43.0 16168 0.5329 0.7479
0.4763 44.0 16544 0.5469 0.7462
0.4775 45.0 16920 0.5538 0.7496
0.4053 46.0 17296 0.5298 0.7613
0.429 47.0 17672 0.5338 0.7663
0.4194 48.0 18048 0.5631 0.7496
0.3965 49.0 18424 0.5407 0.7629
0.356 50.0 18800 0.5438 0.7663

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2