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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_fold4
    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: 0.8

smids_5x_deit_tiny_sgd_0001_fold4

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: 0.4936
  • Accuracy: 0.8

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
1.1564 1.0 375 1.1576 0.3767
1.0853 2.0 750 1.0913 0.4
0.9971 3.0 1125 1.0409 0.44
0.9972 4.0 1500 0.9979 0.4683
0.9211 5.0 1875 0.9595 0.4967
0.885 6.0 2250 0.9224 0.535
0.8576 7.0 2625 0.8878 0.5583
0.8551 8.0 3000 0.8542 0.5783
0.8253 9.0 3375 0.8221 0.6017
0.8198 10.0 3750 0.7908 0.6283
0.6752 11.0 4125 0.7615 0.645
0.6508 12.0 4500 0.7343 0.6767
0.6556 13.0 4875 0.7097 0.69
0.7132 14.0 5250 0.6866 0.7083
0.6057 15.0 5625 0.6661 0.7183
0.5722 16.0 6000 0.6478 0.7283
0.5982 17.0 6375 0.6328 0.7333
0.5686 18.0 6750 0.6177 0.735
0.5939 19.0 7125 0.6046 0.7417
0.5225 20.0 7500 0.5938 0.7483
0.5314 21.0 7875 0.5829 0.7567
0.5367 22.0 8250 0.5746 0.765
0.506 23.0 8625 0.5665 0.77
0.5218 24.0 9000 0.5589 0.7717
0.5608 25.0 9375 0.5520 0.7767
0.5255 26.0 9750 0.5459 0.78
0.5248 27.0 10125 0.5406 0.78
0.496 28.0 10500 0.5353 0.78
0.4514 29.0 10875 0.5308 0.785
0.4878 30.0 11250 0.5266 0.785
0.4791 31.0 11625 0.5226 0.785
0.4601 32.0 12000 0.5192 0.785
0.527 33.0 12375 0.5161 0.7867
0.4682 34.0 12750 0.5130 0.785
0.4268 35.0 13125 0.5104 0.7917
0.4602 36.0 13500 0.5080 0.795
0.4456 37.0 13875 0.5057 0.7983
0.4657 38.0 14250 0.5038 0.7983
0.5191 39.0 14625 0.5021 0.7983
0.5029 40.0 15000 0.5005 0.8
0.4811 41.0 15375 0.4991 0.8
0.4466 42.0 15750 0.4979 0.8
0.4615 43.0 16125 0.4969 0.8017
0.4147 44.0 16500 0.4960 0.8
0.4484 45.0 16875 0.4953 0.8
0.4471 46.0 17250 0.4947 0.8
0.4839 47.0 17625 0.4942 0.8
0.4773 48.0 18000 0.4939 0.8
0.4334 49.0 18375 0.4937 0.8
0.4329 50.0 18750 0.4936 0.8

Framework versions

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