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smids_10x_deit_tiny_adamax_0001_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-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_tiny_adamax_0001_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: 0.9181969949916527

smids_10x_deit_tiny_adamax_0001_fold1

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: 0.7859
  • Accuracy: 0.9182

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: 0.0001
  • 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
0.2608 1.0 751 0.2844 0.8898
0.1304 2.0 1502 0.3294 0.8765
0.111 3.0 2253 0.3516 0.9015
0.1003 4.0 3004 0.4446 0.8932
0.034 5.0 3755 0.5205 0.8982
0.0067 6.0 4506 0.6326 0.9015
0.0227 7.0 5257 0.8411 0.8815
0.0262 8.0 6008 0.8754 0.8865
0.0488 9.0 6759 0.7139 0.9098
0.0361 10.0 7510 0.7866 0.8948
0.0107 11.0 8261 0.8081 0.9048
0.0056 12.0 9012 0.7555 0.8998
0.0 13.0 9763 0.8196 0.9015
0.0016 14.0 10514 0.8589 0.9032
0.0 15.0 11265 0.8346 0.9098
0.0 16.0 12016 0.7703 0.9115
0.0 17.0 12767 0.8587 0.9032
0.0 18.0 13518 0.8122 0.9115
0.0 19.0 14269 0.8002 0.9048
0.0 20.0 15020 0.8446 0.9115
0.0 21.0 15771 0.8926 0.9048
0.0 22.0 16522 0.8190 0.9065
0.0 23.0 17273 0.7943 0.9098
0.0 24.0 18024 0.7616 0.9098
0.0 25.0 18775 0.7566 0.9149
0.0 26.0 19526 0.7309 0.9149
0.0 27.0 20277 0.7760 0.9032
0.0 28.0 21028 0.7849 0.9132
0.008 29.0 21779 0.7826 0.9149
0.0 30.0 22530 0.7666 0.9199
0.0 31.0 23281 0.7402 0.9199
0.0 32.0 24032 0.7484 0.9199
0.0 33.0 24783 0.7616 0.9165
0.0 34.0 25534 0.7803 0.9149
0.0 35.0 26285 0.7685 0.9199
0.0 36.0 27036 0.7685 0.9165
0.0 37.0 27787 0.7687 0.9199
0.0 38.0 28538 0.7876 0.9199
0.0 39.0 29289 0.7749 0.9215
0.0 40.0 30040 0.7734 0.9165
0.0 41.0 30791 0.7803 0.9199
0.0 42.0 31542 0.7799 0.9182
0.0 43.0 32293 0.7798 0.9182
0.0 44.0 33044 0.7789 0.9182
0.0 45.0 33795 0.7827 0.9199
0.0 46.0 34546 0.7810 0.9182
0.0 47.0 35297 0.7840 0.9182
0.0 48.0 36048 0.7837 0.9199
0.0 49.0 36799 0.7839 0.9199
0.0 50.0 37550 0.7859 0.9182

Framework versions

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