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smids_3x_deit_tiny_adamax_00001_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_3x_deit_tiny_adamax_00001_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: 0.8714524207011686

smids_3x_deit_tiny_adamax_00001_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: 0.9289
  • Accuracy: 0.8715

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: 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
0.49 1.0 226 0.4492 0.8013
0.3613 2.0 452 0.3379 0.8614
0.2434 3.0 678 0.3074 0.8648
0.2553 4.0 904 0.3243 0.8648
0.2473 5.0 1130 0.2827 0.8831
0.1686 6.0 1356 0.3078 0.8765
0.1222 7.0 1582 0.3023 0.8998
0.1406 8.0 1808 0.3325 0.8865
0.0989 9.0 2034 0.3862 0.8798
0.0281 10.0 2260 0.3985 0.8748
0.0373 11.0 2486 0.4395 0.8831
0.0324 12.0 2712 0.4479 0.8898
0.0085 13.0 2938 0.5150 0.8865
0.0336 14.0 3164 0.5239 0.8831
0.0184 15.0 3390 0.5580 0.8798
0.0187 16.0 3616 0.6394 0.8798
0.0341 17.0 3842 0.7055 0.8715
0.0009 18.0 4068 0.6833 0.8698
0.0242 19.0 4294 0.6897 0.8731
0.0002 20.0 4520 0.7463 0.8715
0.0021 21.0 4746 0.7865 0.8664
0.0168 22.0 4972 0.7905 0.8715
0.0077 23.0 5198 0.7986 0.8715
0.0002 24.0 5424 0.8358 0.8715
0.0002 25.0 5650 0.8300 0.8698
0.0001 26.0 5876 0.8435 0.8681
0.0001 27.0 6102 0.8418 0.8681
0.0001 28.0 6328 0.8696 0.8681
0.0 29.0 6554 0.8706 0.8698
0.0001 30.0 6780 0.9033 0.8698
0.0001 31.0 7006 0.9296 0.8681
0.0001 32.0 7232 0.8999 0.8698
0.0096 33.0 7458 0.9062 0.8681
0.0001 34.0 7684 0.9009 0.8715
0.0 35.0 7910 0.8975 0.8765
0.0 36.0 8136 0.9003 0.8748
0.0 37.0 8362 0.9103 0.8731
0.0 38.0 8588 0.9226 0.8664
0.0 39.0 8814 0.9185 0.8698
0.0 40.0 9040 0.9208 0.8715
0.0079 41.0 9266 0.9347 0.8698
0.0103 42.0 9492 0.9073 0.8731
0.0 43.0 9718 0.9457 0.8664
0.0 44.0 9944 0.9277 0.8698
0.0 45.0 10170 0.9217 0.8715
0.0 46.0 10396 0.9203 0.8715
0.0 47.0 10622 0.9223 0.8715
0.0 48.0 10848 0.9286 0.8715
0.0 49.0 11074 0.9289 0.8715
0.0 50.0 11300 0.9289 0.8715

Framework versions

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