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smids_5x_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_5x_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: 0.9048414023372288

smids_5x_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: 0.9277
  • Accuracy: 0.9048

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
0.2576 1.0 376 0.2886 0.8915
0.132 2.0 752 0.3675 0.8881
0.153 3.0 1128 0.3722 0.8815
0.1012 4.0 1504 0.5411 0.8715
0.0774 5.0 1880 0.4743 0.8881
0.0966 6.0 2256 0.5187 0.8831
0.02 7.0 2632 0.7637 0.8548
0.0255 8.0 3008 0.5858 0.8982
0.0535 9.0 3384 0.7179 0.8798
0.0051 10.0 3760 0.4830 0.9132
0.0623 11.0 4136 0.6803 0.8898
0.032 12.0 4512 0.6393 0.8831
0.0049 13.0 4888 0.6430 0.8965
0.0375 14.0 5264 0.6697 0.8948
0.0305 15.0 5640 0.4958 0.9165
0.0041 16.0 6016 0.6462 0.8965
0.0225 17.0 6392 0.6064 0.9065
0.0015 18.0 6768 0.7328 0.8865
0.0129 19.0 7144 0.6712 0.8848
0.0072 20.0 7520 0.7644 0.8881
0.002 21.0 7896 0.6536 0.9065
0.0135 22.0 8272 0.7707 0.8881
0.0245 23.0 8648 0.6111 0.8948
0.0006 24.0 9024 0.7622 0.8881
0.0001 25.0 9400 0.7257 0.9015
0.0065 26.0 9776 0.7266 0.8948
0.0001 27.0 10152 0.7834 0.9082
0.0001 28.0 10528 0.7481 0.9032
0.0047 29.0 10904 0.8083 0.8915
0.0032 30.0 11280 0.7670 0.8948
0.0008 31.0 11656 0.8608 0.8881
0.0001 32.0 12032 0.7792 0.8948
0.0001 33.0 12408 0.8789 0.8932
0.0 34.0 12784 0.7571 0.9015
0.0 35.0 13160 0.7309 0.9115
0.0002 36.0 13536 0.7237 0.9082
0.0 37.0 13912 0.8459 0.9015
0.0 38.0 14288 0.8205 0.9082
0.0 39.0 14664 0.8617 0.9048
0.0 40.0 15040 0.8709 0.8932
0.0 41.0 15416 0.8732 0.8915
0.0 42.0 15792 0.8524 0.8982
0.0 43.0 16168 0.8924 0.9048
0.0 44.0 16544 0.8692 0.8898
0.0 45.0 16920 0.8944 0.9015
0.0031 46.0 17296 0.8984 0.9032
0.0 47.0 17672 0.9119 0.9032
0.0 48.0 18048 0.9192 0.9048
0.0 49.0 18424 0.9260 0.9032
0.0023 50.0 18800 0.9277 0.9048

Framework versions

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