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smids_5x_deit_tiny_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_5x_deit_tiny_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: 0.8831385642737897

smids_5x_deit_tiny_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: 0.9436
  • Accuracy: 0.8831

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
0.3072 1.0 376 0.3156 0.8765
0.1866 2.0 752 0.3111 0.8798
0.1992 3.0 1128 0.2782 0.8881
0.1175 4.0 1504 0.2771 0.8932
0.0885 5.0 1880 0.3109 0.8948
0.0629 6.0 2256 0.3306 0.9015
0.0575 7.0 2632 0.3619 0.8948
0.0255 8.0 3008 0.4740 0.8881
0.0161 9.0 3384 0.5352 0.8765
0.0279 10.0 3760 0.5956 0.8815
0.0384 11.0 4136 0.6406 0.8831
0.0026 12.0 4512 0.7055 0.8748
0.0045 13.0 4888 0.7435 0.8715
0.0194 14.0 5264 0.7535 0.8798
0.0002 15.0 5640 0.8056 0.8748
0.0001 16.0 6016 0.8509 0.8781
0.0001 17.0 6392 0.8210 0.8765
0.0001 18.0 6768 0.8304 0.8781
0.0 19.0 7144 0.8626 0.8798
0.0304 20.0 7520 0.8719 0.8765
0.0 21.0 7896 0.9044 0.8781
0.0001 22.0 8272 0.8924 0.8881
0.0 23.0 8648 0.8973 0.8815
0.0 24.0 9024 0.8953 0.8798
0.0 25.0 9400 0.9328 0.8698
0.0041 26.0 9776 0.9144 0.8781
0.0 27.0 10152 0.9041 0.8798
0.0 28.0 10528 0.9154 0.8815
0.0022 29.0 10904 0.9165 0.8831
0.0086 30.0 11280 0.9177 0.8815
0.0 31.0 11656 0.9151 0.8815
0.0 32.0 12032 0.9187 0.8765
0.0 33.0 12408 0.9203 0.8815
0.0 34.0 12784 0.9308 0.8781
0.0 35.0 13160 0.9377 0.8781
0.0 36.0 13536 0.9294 0.8765
0.0 37.0 13912 0.9284 0.8831
0.0 38.0 14288 0.9287 0.8831
0.0 39.0 14664 0.9291 0.8831
0.0 40.0 15040 0.9319 0.8815
0.0 41.0 15416 0.9457 0.8781
0.0 42.0 15792 0.9438 0.8781
0.0 43.0 16168 0.9428 0.8798
0.0 44.0 16544 0.9398 0.8831
0.0 45.0 16920 0.9423 0.8831
0.0059 46.0 17296 0.9455 0.8781
0.0 47.0 17672 0.9433 0.8831
0.0 48.0 18048 0.9441 0.8815
0.0 49.0 18424 0.9445 0.8815
0.0047 50.0 18800 0.9436 0.8831

Framework versions

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