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smids_3x_deit_small_rms_0001_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_3x_deit_small_rms_0001_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: 0.9065108514190318

smids_3x_deit_small_rms_0001_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: 0.8635
  • Accuracy: 0.9065

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
0.3954 1.0 226 0.3893 0.8280
0.2465 2.0 452 0.3428 0.8881
0.1728 3.0 678 0.3745 0.8831
0.1347 4.0 904 0.4194 0.8848
0.0709 5.0 1130 0.4431 0.8898
0.0807 6.0 1356 0.4957 0.8781
0.0722 7.0 1582 0.4748 0.8898
0.036 8.0 1808 0.6178 0.8831
0.0808 9.0 2034 0.5850 0.8831
0.0404 10.0 2260 0.5350 0.9015
0.016 11.0 2486 0.5574 0.8831
0.0147 12.0 2712 0.5709 0.8865
0.0113 13.0 2938 0.6888 0.8815
0.0209 14.0 3164 0.4757 0.9149
0.0245 15.0 3390 0.6913 0.8815
0.0203 16.0 3616 0.6653 0.8865
0.0109 17.0 3842 0.7353 0.8898
0.0341 18.0 4068 0.7660 0.8865
0.0053 19.0 4294 0.6013 0.8965
0.0015 20.0 4520 0.6073 0.8965
0.0003 21.0 4746 0.7366 0.8965
0.0274 22.0 4972 0.7587 0.8798
0.0019 23.0 5198 0.6702 0.8998
0.0001 24.0 5424 0.7767 0.8815
0.0008 25.0 5650 0.6634 0.8998
0.023 26.0 5876 0.7380 0.8915
0.0 27.0 6102 0.8025 0.8898
0.0797 28.0 6328 0.7171 0.8948
0.0492 29.0 6554 0.6827 0.8982
0.0 30.0 6780 0.7690 0.9048
0.0 31.0 7006 0.7411 0.9048
0.0 32.0 7232 0.7425 0.8965
0.0032 33.0 7458 0.7178 0.9115
0.0006 34.0 7684 0.7893 0.9082
0.0 35.0 7910 0.8185 0.8932
0.0181 36.0 8136 0.8745 0.8932
0.0003 37.0 8362 0.8672 0.8932
0.0 38.0 8588 0.8314 0.8982
0.0 39.0 8814 0.8333 0.8965
0.0 40.0 9040 0.7854 0.9065
0.0036 41.0 9266 0.8828 0.9015
0.0028 42.0 9492 0.8402 0.9065
0.0 43.0 9718 0.8689 0.8982
0.0 44.0 9944 0.8390 0.9065
0.0 45.0 10170 0.8434 0.9082
0.0 46.0 10396 0.8531 0.9132
0.0 47.0 10622 0.8589 0.9098
0.0 48.0 10848 0.8632 0.9065
0.0 49.0 11074 0.8630 0.9065
0.0 50.0 11300 0.8635 0.9065

Framework versions

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