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smids_5x_deit_tiny_rms_0001_fold5

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_5x_deit_tiny_rms_0001_fold5
    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: 0.9066666666666666

smids_5x_deit_tiny_rms_0001_fold5

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: 0.9895
  • Accuracy: 0.9067

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
0.3055 1.0 375 0.4440 0.825
0.2222 2.0 750 0.3224 0.8867
0.2186 3.0 1125 0.3702 0.8883
0.1592 4.0 1500 0.4759 0.85
0.0922 5.0 1875 0.4560 0.8767
0.0977 6.0 2250 0.5531 0.875
0.0567 7.0 2625 0.5054 0.8883
0.0612 8.0 3000 0.5016 0.9067
0.0471 9.0 3375 0.6558 0.895
0.0783 10.0 3750 0.7144 0.89
0.0337 11.0 4125 0.7483 0.8833
0.0522 12.0 4500 0.6408 0.8967
0.0122 13.0 4875 0.5578 0.8917
0.0456 14.0 5250 0.6886 0.9
0.0505 15.0 5625 0.6222 0.9067
0.0186 16.0 6000 0.7341 0.8867
0.0232 17.0 6375 0.6650 0.9083
0.0384 18.0 6750 0.6731 0.9133
0.0134 19.0 7125 0.7917 0.8883
0.0197 20.0 7500 0.7544 0.9033
0.006 21.0 7875 0.7694 0.8983
0.084 22.0 8250 0.7873 0.8917
0.0405 23.0 8625 0.7521 0.8967
0.0002 24.0 9000 0.9409 0.8883
0.0009 25.0 9375 0.8364 0.8967
0.0273 26.0 9750 0.7668 0.8933
0.001 27.0 10125 0.7995 0.88
0.0262 28.0 10500 0.8060 0.8883
0.0003 29.0 10875 0.7588 0.9083
0.0189 30.0 11250 0.9019 0.8867
0.0003 31.0 11625 1.0397 0.8867
0.0 32.0 12000 0.9253 0.895
0.0002 33.0 12375 0.8619 0.905
0.0003 34.0 12750 0.9328 0.9
0.0 35.0 13125 0.9364 0.905
0.0002 36.0 13500 0.9470 0.8967
0.0001 37.0 13875 0.9360 0.9033
0.0033 38.0 14250 1.0063 0.9033
0.0 39.0 14625 0.9618 0.9017
0.0 40.0 15000 0.9713 0.9083
0.0 41.0 15375 0.9440 0.9083
0.0 42.0 15750 0.9330 0.91
0.0 43.0 16125 0.9519 0.9083
0.0 44.0 16500 0.9407 0.905
0.0 45.0 16875 0.9804 0.9033
0.0 46.0 17250 0.9891 0.9033
0.0031 47.0 17625 0.9794 0.9033
0.0 48.0 18000 0.9842 0.9033
0.0 49.0 18375 0.9888 0.9067
0.0021 50.0 18750 0.9895 0.9067

Framework versions

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