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smids_3x_deit_small_sgd_0001_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-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_sgd_0001_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: 0.7820299500831946

smids_3x_deit_small_sgd_0001_fold2

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: 0.5927
  • Accuracy: 0.7820

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
1.0529 1.0 225 1.0464 0.4542
1.0393 2.0 450 1.0215 0.4759
1.0194 3.0 675 0.9971 0.5158
0.9608 4.0 900 0.9729 0.5541
0.9743 5.0 1125 0.9487 0.6023
0.9002 6.0 1350 0.9258 0.6206
0.8961 7.0 1575 0.9030 0.6373
0.9282 8.0 1800 0.8813 0.6539
0.856 9.0 2025 0.8605 0.6705
0.8441 10.0 2250 0.8407 0.6772
0.8723 11.0 2475 0.8225 0.6839
0.7789 12.0 2700 0.8048 0.6955
0.7952 13.0 2925 0.7885 0.7055
0.7937 14.0 3150 0.7729 0.7155
0.8007 15.0 3375 0.7585 0.7255
0.769 16.0 3600 0.7449 0.7238
0.7262 17.0 3825 0.7325 0.7255
0.7259 18.0 4050 0.7208 0.7238
0.7176 19.0 4275 0.7099 0.7255
0.6791 20.0 4500 0.6998 0.7271
0.7106 21.0 4725 0.6905 0.7338
0.6951 22.0 4950 0.6819 0.7371
0.7193 23.0 5175 0.6739 0.7471
0.6759 24.0 5400 0.6663 0.7521
0.6975 25.0 5625 0.6593 0.7537
0.6391 26.0 5850 0.6529 0.7571
0.6617 27.0 6075 0.6469 0.7604
0.6434 28.0 6300 0.6413 0.7604
0.6619 29.0 6525 0.6362 0.7587
0.6444 30.0 6750 0.6315 0.7571
0.6161 31.0 6975 0.6270 0.7604
0.6193 32.0 7200 0.6230 0.7671
0.5926 33.0 7425 0.6193 0.7654
0.5861 34.0 7650 0.6159 0.7754
0.6256 35.0 7875 0.6127 0.7770
0.6099 36.0 8100 0.6099 0.7754
0.5932 37.0 8325 0.6073 0.7770
0.5988 38.0 8550 0.6049 0.7804
0.574 39.0 8775 0.6028 0.7787
0.5835 40.0 9000 0.6009 0.7787
0.5292 41.0 9225 0.5992 0.7787
0.586 42.0 9450 0.5977 0.7804
0.5537 43.0 9675 0.5964 0.7820
0.5573 44.0 9900 0.5953 0.7837
0.5715 45.0 10125 0.5945 0.7820
0.6072 46.0 10350 0.5938 0.7820
0.5714 47.0 10575 0.5933 0.7837
0.5684 48.0 10800 0.5929 0.7820
0.5949 49.0 11025 0.5927 0.7820
0.5423 50.0 11250 0.5927 0.7820

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2