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smids_10x_beit_large_adamax_00001_fold1

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_beit_large_adamax_00001_fold1
    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: 0.9282136894824707

smids_10x_beit_large_adamax_00001_fold1

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

  • Loss: 0.8887
  • Accuracy: 0.9282

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
0.1288 1.0 751 0.2785 0.9065
0.0676 2.0 1502 0.3146 0.9149
0.0264 3.0 2253 0.4181 0.9115
0.025 4.0 3004 0.5488 0.9199
0.0069 5.0 3755 0.5526 0.9182
0.0049 6.0 4506 0.6296 0.9165
0.0005 7.0 5257 0.7054 0.9149
0.0001 8.0 6008 0.7404 0.9182
0.0362 9.0 6759 0.7520 0.9132
0.0001 10.0 7510 0.8011 0.9149
0.0001 11.0 8261 0.7591 0.9199
0.0002 12.0 9012 0.7216 0.9215
0.0024 13.0 9763 0.8101 0.9132
0.0 14.0 10514 0.8382 0.9249
0.0 15.0 11265 0.8571 0.9165
0.0 16.0 12016 0.8307 0.9249
0.0002 17.0 12767 0.8135 0.9098
0.0 18.0 13518 0.9070 0.9132
0.0 19.0 14269 0.8650 0.9115
0.0 20.0 15020 0.8297 0.9265
0.0 21.0 15771 0.8359 0.9282
0.0 22.0 16522 0.8827 0.9265
0.0 23.0 17273 0.8484 0.9215
0.0 24.0 18024 0.8739 0.9182
0.0004 25.0 18775 0.8728 0.9232
0.0 26.0 19526 0.8742 0.9149
0.0 27.0 20277 0.9029 0.9199
0.0 28.0 21028 0.8812 0.9232
0.0109 29.0 21779 0.9326 0.9215
0.0 30.0 22530 0.9197 0.9115
0.0001 31.0 23281 0.8910 0.9215
0.0 32.0 24032 0.8659 0.9215
0.0 33.0 24783 0.8759 0.9232
0.0 34.0 25534 0.9176 0.9199
0.0 35.0 26285 0.8674 0.9249
0.0 36.0 27036 0.8364 0.9249
0.0 37.0 27787 0.8518 0.9265
0.0 38.0 28538 0.8614 0.9232
0.0 39.0 29289 0.8789 0.9215
0.0 40.0 30040 0.8979 0.9215
0.0 41.0 30791 0.9262 0.9199
0.0107 42.0 31542 0.8969 0.9232
0.0 43.0 32293 0.9021 0.9265
0.0 44.0 33044 0.8921 0.9282
0.0 45.0 33795 0.9002 0.9249
0.0007 46.0 34546 0.9147 0.9199
0.0 47.0 35297 0.8904 0.9249
0.0 48.0 36048 0.8842 0.9282
0.0 49.0 36799 0.8899 0.9265
0.0 50.0 37550 0.8887 0.9282

Framework versions

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