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smids_10x_deit_tiny_adamax_00001_fold2

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_10x_deit_tiny_adamax_00001_fold2
    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: 0.8935108153078203

smids_10x_deit_tiny_adamax_00001_fold2

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: 0.9769
  • Accuracy: 0.8935

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
0.2848 1.0 750 0.3201 0.8702
0.1766 2.0 1500 0.2882 0.8869
0.1715 3.0 2250 0.2793 0.8918
0.1528 4.0 3000 0.2928 0.8918
0.1474 5.0 3750 0.3209 0.8902
0.0904 6.0 4500 0.3386 0.8835
0.0506 7.0 5250 0.4033 0.8918
0.0528 8.0 6000 0.4656 0.8985
0.0576 9.0 6750 0.4872 0.8852
0.0247 10.0 7500 0.5636 0.8968
0.0283 11.0 8250 0.5957 0.8918
0.017 12.0 9000 0.6715 0.8952
0.0013 13.0 9750 0.7105 0.8885
0.0004 14.0 10500 0.7432 0.8819
0.0011 15.0 11250 0.7702 0.8968
0.0001 16.0 12000 0.8390 0.8935
0.0001 17.0 12750 0.8598 0.8918
0.0002 18.0 13500 0.8606 0.8852
0.0 19.0 14250 0.8893 0.8902
0.0001 20.0 15000 0.9092 0.8869
0.0 21.0 15750 0.9243 0.8935
0.0414 22.0 16500 0.8856 0.8935
0.0196 23.0 17250 0.9361 0.8902
0.0 24.0 18000 0.9456 0.8952
0.0 25.0 18750 0.9417 0.8935
0.0 26.0 19500 0.9178 0.8968
0.0233 27.0 20250 0.9491 0.8902
0.0 28.0 21000 0.9447 0.9002
0.0 29.0 21750 0.9458 0.8952
0.0 30.0 22500 0.9429 0.8935
0.0 31.0 23250 0.9485 0.8902
0.0 32.0 24000 0.9592 0.8918
0.0 33.0 24750 0.9720 0.8935
0.0 34.0 25500 0.9605 0.8918
0.0 35.0 26250 0.9711 0.8952
0.0 36.0 27000 0.9779 0.8902
0.0248 37.0 27750 0.9824 0.8918
0.0 38.0 28500 0.9776 0.8968
0.0 39.0 29250 0.9729 0.8968
0.0 40.0 30000 0.9687 0.8952
0.0 41.0 30750 0.9781 0.8952
0.0 42.0 31500 0.9860 0.8918
0.0 43.0 32250 0.9836 0.8918
0.0 44.0 33000 0.9765 0.8952
0.0004 45.0 33750 0.9803 0.8935
0.0 46.0 34500 0.9754 0.8952
0.0 47.0 35250 0.9749 0.8952
0.0 48.0 36000 0.9757 0.8952
0.0 49.0 36750 0.9765 0.8935
0.0 50.0 37500 0.9769 0.8935

Framework versions

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