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hushem_40x_deit_tiny_sgd_00001_fold5

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_sgd_00001_fold5
    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: 0.1951219512195122

hushem_40x_deit_tiny_sgd_00001_fold5

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: 1.5613
  • Accuracy: 0.1951

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
1.4842 1.0 220 1.6446 0.1951
1.5035 2.0 440 1.6414 0.1951
1.4995 3.0 660 1.6381 0.1951
1.5021 4.0 880 1.6349 0.1707
1.454 5.0 1100 1.6317 0.1707
1.4629 6.0 1320 1.6285 0.1707
1.4161 7.0 1540 1.6253 0.1707
1.4101 8.0 1760 1.6223 0.1707
1.4149 9.0 1980 1.6192 0.1707
1.4443 10.0 2200 1.6162 0.1707
1.4163 11.0 2420 1.6133 0.1707
1.4351 12.0 2640 1.6104 0.1707
1.4104 13.0 2860 1.6076 0.1707
1.3915 14.0 3080 1.6048 0.1707
1.4251 15.0 3300 1.6022 0.1707
1.4091 16.0 3520 1.5996 0.1951
1.384 17.0 3740 1.5971 0.1951
1.3979 18.0 3960 1.5947 0.1951
1.3842 19.0 4180 1.5923 0.1951
1.3555 20.0 4400 1.5900 0.1951
1.3519 21.0 4620 1.5879 0.1951
1.3873 22.0 4840 1.5859 0.1951
1.3791 23.0 5060 1.5839 0.1951
1.3799 24.0 5280 1.5820 0.1951
1.3568 25.0 5500 1.5802 0.1951
1.369 26.0 5720 1.5786 0.1951
1.3732 27.0 5940 1.5770 0.1951
1.3491 28.0 6160 1.5754 0.1951
1.3457 29.0 6380 1.5740 0.1951
1.3169 30.0 6600 1.5726 0.1951
1.3748 31.0 6820 1.5714 0.1951
1.3384 32.0 7040 1.5702 0.1951
1.3281 33.0 7260 1.5691 0.1951
1.3359 34.0 7480 1.5681 0.1951
1.3414 35.0 7700 1.5671 0.1951
1.3339 36.0 7920 1.5662 0.1951
1.3778 37.0 8140 1.5654 0.1951
1.3669 38.0 8360 1.5647 0.1951
1.3509 39.0 8580 1.5641 0.1951
1.3269 40.0 8800 1.5635 0.1951
1.3717 41.0 9020 1.5630 0.1951
1.3455 42.0 9240 1.5626 0.1951
1.3737 43.0 9460 1.5622 0.1951
1.3166 44.0 9680 1.5619 0.1951
1.3504 45.0 9900 1.5617 0.1951
1.3509 46.0 10120 1.5615 0.1951
1.3526 47.0 10340 1.5614 0.1951
1.3222 48.0 10560 1.5613 0.1951
1.3165 49.0 10780 1.5613 0.1951
1.3501 50.0 11000 1.5613 0.1951

Framework versions

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