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smids_5x_deit_tiny_sgd_001_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_sgd_001_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: 0.8914858096828047

smids_5x_deit_tiny_sgd_001_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: 0.2974
  • Accuracy: 0.8915

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
0.7214 1.0 376 0.7354 0.7112
0.5305 2.0 752 0.5484 0.7780
0.423 3.0 1128 0.4775 0.8063
0.4098 4.0 1504 0.4302 0.8364
0.4286 5.0 1880 0.4059 0.8497
0.3605 6.0 2256 0.3872 0.8548
0.3093 7.0 2632 0.3738 0.8648
0.3348 8.0 3008 0.3632 0.8664
0.3284 9.0 3384 0.3510 0.8765
0.3008 10.0 3760 0.3447 0.8748
0.289 11.0 4136 0.3398 0.8798
0.2542 12.0 4512 0.3320 0.8848
0.245 13.0 4888 0.3263 0.8865
0.2258 14.0 5264 0.3225 0.8865
0.3082 15.0 5640 0.3188 0.8848
0.2685 16.0 6016 0.3171 0.8848
0.2379 17.0 6392 0.3137 0.8865
0.2778 18.0 6768 0.3111 0.8848
0.2374 19.0 7144 0.3083 0.8848
0.1845 20.0 7520 0.3061 0.8848
0.2126 21.0 7896 0.3049 0.8865
0.2068 22.0 8272 0.3078 0.8831
0.2364 23.0 8648 0.3060 0.8798
0.1851 24.0 9024 0.3035 0.8881
0.2035 25.0 9400 0.3013 0.8848
0.2146 26.0 9776 0.3016 0.8881
0.1495 27.0 10152 0.2986 0.8915
0.1962 28.0 10528 0.2989 0.8898
0.2019 29.0 10904 0.2993 0.8881
0.1531 30.0 11280 0.2975 0.8932
0.1643 31.0 11656 0.2990 0.8898
0.2082 32.0 12032 0.2991 0.8881
0.1845 33.0 12408 0.2980 0.8915
0.1333 34.0 12784 0.2976 0.8932
0.1524 35.0 13160 0.3000 0.8865
0.1908 36.0 13536 0.2977 0.8915
0.1391 37.0 13912 0.2964 0.8948
0.1756 38.0 14288 0.2975 0.8915
0.2131 39.0 14664 0.2969 0.8932
0.1588 40.0 15040 0.2977 0.8898
0.1631 41.0 15416 0.2962 0.8932
0.1431 42.0 15792 0.2974 0.8915
0.1556 43.0 16168 0.2976 0.8898
0.1705 44.0 16544 0.2978 0.8915
0.1792 45.0 16920 0.2986 0.8898
0.1949 46.0 17296 0.2975 0.8915
0.1472 47.0 17672 0.2972 0.8915
0.139 48.0 18048 0.2974 0.8915
0.1452 49.0 18424 0.2974 0.8915
0.1388 50.0 18800 0.2974 0.8915

Framework versions

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