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smids_10x_deit_small_adamax_00001_fold1

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-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_small_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: 0.9065108514190318

smids_10x_deit_small_adamax_00001_fold1

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

  • Loss: 0.8282
  • Accuracy: 0.9065

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
0.2758 1.0 751 0.3168 0.8831
0.1955 2.0 1502 0.2645 0.9032
0.1342 3.0 2253 0.2464 0.9115
0.0581 4.0 3004 0.2670 0.9032
0.0977 5.0 3755 0.3303 0.9115
0.0404 6.0 4506 0.3924 0.9048
0.0407 7.0 5257 0.4392 0.9098
0.0229 8.0 6008 0.5277 0.9132
0.023 9.0 6759 0.5759 0.9115
0.016 10.0 7510 0.6280 0.9032
0.0002 11.0 8261 0.6513 0.9098
0.0008 12.0 9012 0.6409 0.9182
0.006 13.0 9763 0.6473 0.9199
0.0 14.0 10514 0.7396 0.9065
0.0 15.0 11265 0.7703 0.9065
0.0 16.0 12016 0.7534 0.9065
0.0001 17.0 12767 0.8086 0.9032
0.0 18.0 13518 0.7937 0.9032
0.0 19.0 14269 0.7606 0.9165
0.0 20.0 15020 0.8234 0.9065
0.0001 21.0 15771 0.7617 0.9149
0.0 22.0 16522 0.8024 0.9015
0.0 23.0 17273 0.8089 0.9065
0.0 24.0 18024 0.8495 0.9015
0.0 25.0 18775 0.7997 0.9115
0.0 26.0 19526 0.8566 0.9015
0.0 27.0 20277 0.8140 0.9065
0.0 28.0 21028 0.8138 0.9065
0.0073 29.0 21779 0.7958 0.9082
0.0 30.0 22530 0.8037 0.9115
0.0 31.0 23281 0.8741 0.9032
0.0 32.0 24032 0.8298 0.9082
0.0 33.0 24783 0.8730 0.9015
0.0 34.0 25534 0.8840 0.8982
0.0 35.0 26285 0.8051 0.9132
0.0 36.0 27036 0.8192 0.9115
0.0 37.0 27787 0.8059 0.9132
0.0 38.0 28538 0.8065 0.9149
0.0 39.0 29289 0.8139 0.9132
0.0 40.0 30040 0.8141 0.9132
0.0 41.0 30791 0.8317 0.9098
0.0 42.0 31542 0.8371 0.9048
0.0 43.0 32293 0.8394 0.9032
0.0 44.0 33044 0.8362 0.9048
0.0 45.0 33795 0.8367 0.9048
0.0 46.0 34546 0.8416 0.9032
0.0 47.0 35297 0.8349 0.9048
0.0 48.0 36048 0.8314 0.9065
0.0 49.0 36799 0.8317 0.9065
0.0 50.0 37550 0.8282 0.9065

Framework versions

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