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End of training
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metadata
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_base_adamax_00001_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: 0.9285714285714286

hushem_5x_deit_base_adamax_00001_fold4

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2070
  • Accuracy: 0.9286

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
1.2779 1.0 28 1.2578 0.5
0.9904 2.0 56 1.0864 0.5952
0.7136 3.0 84 0.8757 0.7381
0.5283 4.0 112 0.7271 0.8095
0.3401 5.0 140 0.5900 0.8333
0.2667 6.0 168 0.4970 0.8571
0.1719 7.0 196 0.4291 0.8810
0.1351 8.0 224 0.3736 0.8810
0.0756 9.0 252 0.3239 0.8571
0.0457 10.0 280 0.2724 0.9286
0.0311 11.0 308 0.2513 0.9286
0.0183 12.0 336 0.2397 0.9524
0.0115 13.0 364 0.2242 0.9286
0.0092 14.0 392 0.2124 0.9286
0.0064 15.0 420 0.2027 0.9286
0.0052 16.0 448 0.2099 0.9286
0.0048 17.0 476 0.2208 0.9286
0.0041 18.0 504 0.2156 0.9286
0.0036 19.0 532 0.2081 0.9286
0.0034 20.0 560 0.2100 0.9286
0.003 21.0 588 0.2099 0.9286
0.0028 22.0 616 0.2113 0.9286
0.0024 23.0 644 0.2110 0.9286
0.0023 24.0 672 0.2106 0.9286
0.0022 25.0 700 0.2101 0.9286
0.002 26.0 728 0.2088 0.9286
0.002 27.0 756 0.2066 0.9286
0.0018 28.0 784 0.2096 0.9286
0.0018 29.0 812 0.2064 0.9286
0.0016 30.0 840 0.2088 0.9286
0.0016 31.0 868 0.2088 0.9286
0.0015 32.0 896 0.2078 0.9286
0.0015 33.0 924 0.2057 0.9286
0.0014 34.0 952 0.2073 0.9286
0.0014 35.0 980 0.2070 0.9286
0.0014 36.0 1008 0.2069 0.9286
0.0013 37.0 1036 0.2071 0.9286
0.0013 38.0 1064 0.2055 0.9286
0.0013 39.0 1092 0.2077 0.9286
0.0011 40.0 1120 0.2076 0.9286
0.0012 41.0 1148 0.2068 0.9286
0.0012 42.0 1176 0.2086 0.9286
0.0011 43.0 1204 0.2084 0.9286
0.0011 44.0 1232 0.2077 0.9286
0.0011 45.0 1260 0.2078 0.9286
0.0011 46.0 1288 0.2072 0.9286
0.0011 47.0 1316 0.2070 0.9286
0.0011 48.0 1344 0.2070 0.9286
0.0012 49.0 1372 0.2070 0.9286
0.0011 50.0 1400 0.2070 0.9286

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0