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smids_3x_deit_small_adamax_0001_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-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_adamax_0001_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: 0.8768718801996672

smids_3x_deit_small_adamax_0001_fold2

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: 1.0636
  • Accuracy: 0.8769

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
0.2927 1.0 225 0.3073 0.8686
0.1512 2.0 450 0.3158 0.8902
0.0739 3.0 675 0.4826 0.8602
0.0696 4.0 900 0.4520 0.8835
0.0095 5.0 1125 0.6442 0.8636
0.0162 6.0 1350 0.8340 0.8403
0.0336 7.0 1575 0.6936 0.8669
0.0025 8.0 1800 0.8353 0.8669
0.0105 9.0 2025 0.8036 0.8702
0.0025 10.0 2250 0.8245 0.8769
0.0004 11.0 2475 0.9095 0.8802
0.0003 12.0 2700 1.0840 0.8669
0.0001 13.0 2925 0.9475 0.8735
0.016 14.0 3150 0.9122 0.8835
0.0 15.0 3375 0.7790 0.8918
0.0 16.0 3600 0.7787 0.8785
0.0029 17.0 3825 0.8308 0.8918
0.0 18.0 4050 0.9812 0.8752
0.0006 19.0 4275 0.9540 0.8686
0.0 20.0 4500 0.9292 0.8785
0.0 21.0 4725 0.9652 0.8785
0.0 22.0 4950 0.9762 0.8802
0.0029 23.0 5175 0.9826 0.8702
0.0 24.0 5400 0.9763 0.8669
0.0047 25.0 5625 0.9733 0.8719
0.0 26.0 5850 0.9680 0.8686
0.0 27.0 6075 0.9775 0.8869
0.0 28.0 6300 0.9726 0.8769
0.0 29.0 6525 1.0293 0.8702
0.0036 30.0 6750 1.0029 0.8885
0.0 31.0 6975 0.9992 0.8686
0.0 32.0 7200 0.9937 0.8686
0.0 33.0 7425 0.9950 0.8802
0.0 34.0 7650 0.9997 0.8735
0.0 35.0 7875 1.0478 0.8769
0.0033 36.0 8100 1.0278 0.8686
0.0 37.0 8325 1.0247 0.8802
0.0 38.0 8550 1.0317 0.8785
0.0032 39.0 8775 1.0297 0.8819
0.0 40.0 9000 1.0396 0.8802
0.0 41.0 9225 1.0404 0.8752
0.0 42.0 9450 1.0476 0.8785
0.0 43.0 9675 1.0513 0.8802
0.0 44.0 9900 1.0502 0.8769
0.0 45.0 10125 1.0584 0.8752
0.0 46.0 10350 1.0568 0.8769
0.0 47.0 10575 1.0576 0.8785
0.0 48.0 10800 1.0616 0.8785
0.0023 49.0 11025 1.0626 0.8769
0.0023 50.0 11250 1.0636 0.8769

Framework versions

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