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smids_3x_deit_tiny_rms_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-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_rms_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_3x_deit_tiny_rms_00001_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: 0.9365
  • 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
0.3997 1.0 226 0.3347 0.8631
0.255 2.0 452 0.2918 0.8831
0.1424 3.0 678 0.3193 0.8748
0.1606 4.0 904 0.3236 0.8865
0.1532 5.0 1130 0.3062 0.8915
0.0588 6.0 1356 0.4276 0.8915
0.0136 7.0 1582 0.4629 0.8831
0.0352 8.0 1808 0.5602 0.8765
0.0656 9.0 2034 0.5379 0.8765
0.0146 10.0 2260 0.6661 0.8881
0.0002 11.0 2486 0.7507 0.8798
0.015 12.0 2712 0.6981 0.8865
0.0138 13.0 2938 0.9249 0.8715
0.0124 14.0 3164 0.8454 0.8748
0.0002 15.0 3390 0.8233 0.8781
0.0006 16.0 3616 0.8574 0.8698
0.0171 17.0 3842 0.8765 0.8781
0.0 18.0 4068 0.8826 0.8865
0.0173 19.0 4294 0.7556 0.8932
0.0158 20.0 4520 0.9424 0.8748
0.0001 21.0 4746 1.0298 0.8648
0.0133 22.0 4972 0.9420 0.8664
0.0145 23.0 5198 0.8626 0.8865
0.0001 24.0 5424 0.9250 0.8781
0.0 25.0 5650 0.8112 0.8948
0.0002 26.0 5876 0.8569 0.8898
0.0 27.0 6102 0.8070 0.8915
0.0 28.0 6328 0.8507 0.8765
0.0 29.0 6554 0.8437 0.8932
0.0 30.0 6780 0.8816 0.8848
0.0 31.0 7006 0.8733 0.8848
0.0 32.0 7232 0.9948 0.8681
0.0082 33.0 7458 0.9148 0.8831
0.0 34.0 7684 0.9131 0.8881
0.0 35.0 7910 0.9403 0.8781
0.0 36.0 8136 0.9014 0.8765
0.0 37.0 8362 0.9056 0.8798
0.0 38.0 8588 0.9375 0.8781
0.0 39.0 8814 0.9025 0.8831
0.0 40.0 9040 0.9205 0.8815
0.0051 41.0 9266 0.9089 0.8848
0.0025 42.0 9492 0.9223 0.8848
0.0 43.0 9718 0.9136 0.8881
0.0 44.0 9944 0.9207 0.8848
0.0 45.0 10170 0.9266 0.8831
0.0 46.0 10396 0.9325 0.8865
0.0 47.0 10622 0.9382 0.8815
0.0 48.0 10848 0.9372 0.8815
0.0 49.0 11074 0.9372 0.8831
0.0 50.0 11300 0.9365 0.8831

Framework versions

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