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hushem_1x_deit_tiny_adamax_lr001_fold5

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metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_adamax_lr001_fold5
    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: 0.6585365853658537

hushem_1x_deit_tiny_adamax_lr001_fold5

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

  • Loss: 1.1657
  • Accuracy: 0.6585

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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
No log 0.67 1 4.7722 0.2439
No log 2.0 3 1.4567 0.2439
No log 2.67 4 1.8233 0.2683
No log 4.0 6 1.3918 0.2439
No log 4.67 7 1.4247 0.2195
No log 6.0 9 1.3988 0.2439
1.9646 6.67 10 1.3700 0.3415
1.9646 8.0 12 1.3164 0.3902
1.9646 8.67 13 1.2953 0.3902
1.9646 10.0 15 1.0825 0.5366
1.9646 10.67 16 0.9280 0.7561
1.9646 12.0 18 0.9474 0.5610
1.9646 12.67 19 0.9791 0.5122
1.1934 14.0 21 1.3039 0.3902
1.1934 14.67 22 1.3242 0.3902
1.1934 16.0 24 0.8880 0.6341
1.1934 16.67 25 0.8367 0.6341
1.1934 18.0 27 0.8476 0.6098
1.1934 18.67 28 0.9406 0.5854
0.8297 20.0 30 1.1819 0.4878
0.8297 20.67 31 0.9194 0.5610
0.8297 22.0 33 0.7486 0.6829
0.8297 22.67 34 1.1493 0.6341
0.8297 24.0 36 1.2217 0.5854
0.8297 24.67 37 0.7746 0.6829
0.8297 26.0 39 0.8320 0.6585
0.5433 26.67 40 1.2210 0.5610
0.5433 28.0 42 1.3782 0.5366
0.5433 28.67 43 1.1529 0.6098
0.5433 30.0 45 1.0361 0.6585
0.5433 30.67 46 1.1089 0.6585
0.5433 32.0 48 1.1802 0.6098
0.5433 32.67 49 1.1774 0.6585
0.2758 33.33 50 1.1657 0.6585

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1