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hushem_40x_deit_tiny_rms_001_fold3

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: hushem_40x_deit_tiny_rms_001_fold3
    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: 0.7906976744186046

hushem_40x_deit_tiny_rms_001_fold3

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: 2.2392
  • Accuracy: 0.7907

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
1.246 1.0 217 1.4100 0.2558
0.9627 2.0 434 1.1709 0.5116
0.8333 3.0 651 1.1793 0.4186
0.7844 4.0 868 0.8077 0.6279
0.7035 5.0 1085 1.3327 0.5116
0.7976 6.0 1302 0.7941 0.6977
0.7352 7.0 1519 0.9909 0.6279
0.6247 8.0 1736 0.7281 0.6977
0.6212 9.0 1953 1.1902 0.6279
0.6647 10.0 2170 1.0897 0.5581
0.4763 11.0 2387 0.9383 0.6047
0.5076 12.0 2604 0.5861 0.7907
0.4573 13.0 2821 0.9438 0.5349
0.3857 14.0 3038 0.7991 0.6977
0.3919 15.0 3255 0.9377 0.6047
0.352 16.0 3472 1.0859 0.5814
0.3551 17.0 3689 1.2113 0.6744
0.3196 18.0 3906 1.2889 0.6279
0.2405 19.0 4123 0.9915 0.6512
0.2367 20.0 4340 1.6136 0.6279
0.2222 21.0 4557 1.4836 0.5814
0.1901 22.0 4774 1.0739 0.7209
0.173 23.0 4991 1.3956 0.6512
0.1711 24.0 5208 1.7072 0.6279
0.1027 25.0 5425 1.4657 0.6512
0.0952 26.0 5642 1.6372 0.6744
0.1462 27.0 5859 2.2566 0.5814
0.1003 28.0 6076 1.5093 0.6512
0.0764 29.0 6293 1.9318 0.6512
0.1025 30.0 6510 1.9630 0.6047
0.0702 31.0 6727 2.1273 0.6512
0.0313 32.0 6944 1.6171 0.7209
0.0732 33.0 7161 1.2147 0.7209
0.0384 34.0 7378 1.9804 0.7209
0.0177 35.0 7595 1.8221 0.6512
0.0098 36.0 7812 2.4941 0.6744
0.0407 37.0 8029 2.6063 0.6512
0.0798 38.0 8246 3.5391 0.5581
0.0022 39.0 8463 2.7971 0.6512
0.0004 40.0 8680 1.8602 0.7209
0.0547 41.0 8897 2.4427 0.6744
0.0003 42.0 9114 2.1061 0.6977
0.0003 43.0 9331 2.0283 0.5814
0.0017 44.0 9548 2.1926 0.6744
0.0001 45.0 9765 1.9704 0.7674
0.0 46.0 9982 2.2645 0.7442
0.0001 47.0 10199 2.3408 0.7674
0.0 48.0 10416 2.2312 0.7674
0.0 49.0 10633 2.2302 0.7907
0.0 50.0 10850 2.2392 0.7907

Framework versions

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