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smids_10x_deit_tiny_sgd_001_fold2

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_sgd_001_fold2
    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: 0.8901830282861897

smids_10x_deit_tiny_sgd_001_fold2

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: 0.3073
  • Accuracy: 0.8902

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: 0.001
  • 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
0.5172 1.0 750 0.5682 0.7687
0.3347 2.0 1500 0.4469 0.8153
0.3177 3.0 2250 0.3953 0.8469
0.3776 4.0 3000 0.3688 0.8502
0.2886 5.0 3750 0.3556 0.8519
0.2396 6.0 4500 0.3328 0.8502
0.2545 7.0 5250 0.3237 0.8586
0.2435 8.0 6000 0.3188 0.8569
0.2366 9.0 6750 0.3065 0.8686
0.232 10.0 7500 0.3041 0.8652
0.2399 11.0 8250 0.2971 0.8785
0.2717 12.0 9000 0.2941 0.8769
0.2579 13.0 9750 0.2863 0.8852
0.1661 14.0 10500 0.2895 0.8802
0.1655 15.0 11250 0.2865 0.8785
0.1921 16.0 12000 0.2897 0.8802
0.1525 17.0 12750 0.2854 0.8835
0.1653 18.0 13500 0.2861 0.8819
0.1849 19.0 14250 0.2939 0.8702
0.1923 20.0 15000 0.2850 0.8835
0.1967 21.0 15750 0.2874 0.8802
0.1373 22.0 16500 0.2916 0.8802
0.1229 23.0 17250 0.2891 0.8869
0.1054 24.0 18000 0.2911 0.8802
0.1456 25.0 18750 0.2869 0.8869
0.2052 26.0 19500 0.2987 0.8835
0.1723 27.0 20250 0.2918 0.8835
0.1277 28.0 21000 0.2937 0.8902
0.1569 29.0 21750 0.2956 0.8902
0.1514 30.0 22500 0.2954 0.8885
0.1603 31.0 23250 0.2954 0.8902
0.1428 32.0 24000 0.2940 0.8918
0.1564 33.0 24750 0.3002 0.8835
0.1386 34.0 25500 0.3023 0.8852
0.1564 35.0 26250 0.2982 0.8869
0.183 36.0 27000 0.3004 0.8885
0.1456 37.0 27750 0.3058 0.8869
0.1394 38.0 28500 0.3047 0.8869
0.121 39.0 29250 0.3021 0.8902
0.1192 40.0 30000 0.3035 0.8852
0.1706 41.0 30750 0.3048 0.8918
0.1421 42.0 31500 0.3036 0.8885
0.1223 43.0 32250 0.3066 0.8852
0.1116 44.0 33000 0.3060 0.8885
0.1122 45.0 33750 0.3075 0.8885
0.1411 46.0 34500 0.3066 0.8885
0.1644 47.0 35250 0.3072 0.8885
0.0953 48.0 36000 0.3070 0.8902
0.1109 49.0 36750 0.3072 0.8902
0.1061 50.0 37500 0.3073 0.8902

Framework versions

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