hkivancoral's picture
End of training
82af3ba
metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_beit_base_rms_00001_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: 0.8372093023255814

hushem_1x_beit_base_rms_00001_fold3

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

  • Loss: 0.5862
  • Accuracy: 0.8372

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
No log 1.0 6 1.3652 0.2558
1.4655 2.0 12 0.9320 0.6512
1.4655 3.0 18 0.5733 0.7907
0.6613 4.0 24 0.3842 0.8605
0.1719 5.0 30 0.4268 0.8605
0.1719 6.0 36 0.3122 0.8837
0.0362 7.0 42 0.5635 0.7907
0.0362 8.0 48 0.2839 0.8837
0.0103 9.0 54 0.3515 0.9070
0.0048 10.0 60 0.4717 0.8837
0.0048 11.0 66 0.4775 0.8372
0.0038 12.0 72 0.5321 0.7907
0.0038 13.0 78 0.4659 0.8372
0.0022 14.0 84 0.5318 0.8140
0.0017 15.0 90 0.5328 0.8605
0.0017 16.0 96 0.4991 0.8372
0.0025 17.0 102 0.5203 0.8372
0.0025 18.0 108 0.5439 0.8372
0.0011 19.0 114 0.5049 0.8372
0.0014 20.0 120 0.5023 0.8372
0.0014 21.0 126 0.5748 0.8372
0.0013 22.0 132 0.5341 0.8372
0.0013 23.0 138 0.4866 0.8372
0.0011 24.0 144 0.5270 0.8372
0.0012 25.0 150 0.5889 0.8372
0.0012 26.0 156 0.6180 0.8372
0.0013 27.0 162 0.6227 0.8372
0.0013 28.0 168 0.6125 0.8372
0.0007 29.0 174 0.5708 0.8605
0.0004 30.0 180 0.5729 0.8372
0.0004 31.0 186 0.5789 0.8372
0.001 32.0 192 0.5842 0.8140
0.001 33.0 198 0.5989 0.8372
0.0008 34.0 204 0.5775 0.8140
0.0013 35.0 210 0.5738 0.8372
0.0013 36.0 216 0.5742 0.8140
0.0006 37.0 222 0.6172 0.8140
0.0006 38.0 228 0.5958 0.8140
0.0026 39.0 234 0.5884 0.8140
0.0006 40.0 240 0.5885 0.8140
0.0006 41.0 246 0.5863 0.8372
0.0008 42.0 252 0.5862 0.8372
0.0008 43.0 258 0.5862 0.8372
0.0006 44.0 264 0.5862 0.8372
0.0004 45.0 270 0.5862 0.8372
0.0004 46.0 276 0.5862 0.8372
0.0006 47.0 282 0.5862 0.8372
0.0006 48.0 288 0.5862 0.8372
0.0005 49.0 294 0.5862 0.8372
0.0004 50.0 300 0.5862 0.8372

Framework versions

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