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smids_5x_deit_base_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-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_base_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: 0.9081803005008348

smids_5x_deit_base_rms_0001_fold1

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8795
  • Accuracy: 0.9082

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
0.232 1.0 376 0.3558 0.8531
0.1372 2.0 752 0.3215 0.8898
0.1203 3.0 1128 0.3886 0.8881
0.0697 4.0 1504 0.4340 0.8765
0.0324 5.0 1880 0.5132 0.8865
0.0728 6.0 2256 0.5310 0.8881
0.0495 7.0 2632 0.6186 0.8781
0.0797 8.0 3008 0.5758 0.8881
0.0121 9.0 3384 0.5420 0.8998
0.0399 10.0 3760 0.6888 0.8815
0.0293 11.0 4136 0.5816 0.9065
0.0382 12.0 4512 0.6430 0.8781
0.0148 13.0 4888 0.8464 0.8781
0.0249 14.0 5264 0.5972 0.8848
0.0008 15.0 5640 0.6413 0.8965
0.0409 16.0 6016 0.7158 0.8798
0.0202 17.0 6392 0.7167 0.8815
0.0253 18.0 6768 0.6005 0.8998
0.0002 19.0 7144 0.6775 0.8948
0.0275 20.0 7520 0.7685 0.8948
0.0272 21.0 7896 0.6847 0.8965
0.0003 22.0 8272 0.7613 0.8915
0.0008 23.0 8648 0.7342 0.8865
0.0001 24.0 9024 0.7590 0.8698
0.0 25.0 9400 0.7885 0.8898
0.0032 26.0 9776 0.6867 0.8982
0.0 27.0 10152 0.7507 0.8948
0.0003 28.0 10528 0.7142 0.8848
0.0043 29.0 10904 0.6904 0.8881
0.0066 30.0 11280 0.7736 0.8898
0.0007 31.0 11656 0.7358 0.8998
0.0006 32.0 12032 1.0875 0.8598
0.0 33.0 12408 0.7340 0.9015
0.0 34.0 12784 0.7139 0.8982
0.0 35.0 13160 0.7525 0.9115
0.0002 36.0 13536 0.7504 0.8982
0.0 37.0 13912 0.8006 0.8982
0.0 38.0 14288 0.7615 0.9015
0.0 39.0 14664 0.7609 0.9115
0.0 40.0 15040 0.8059 0.9015
0.0 41.0 15416 0.8037 0.9032
0.0 42.0 15792 0.8697 0.9048
0.0 43.0 16168 0.8414 0.9115
0.0 44.0 16544 0.8687 0.9098
0.0 45.0 16920 0.8833 0.9065
0.0031 46.0 17296 0.8963 0.9065
0.0 47.0 17672 0.8765 0.9082
0.0 48.0 18048 0.8724 0.9082
0.0 49.0 18424 0.8783 0.9082
0.0025 50.0 18800 0.8795 0.9082

Framework versions

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