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hushem_40x_deit_base_sgd_001_fold4

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: hushem_40x_deit_base_sgd_001_fold4
    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: 0.9285714285714286

hushem_40x_deit_base_sgd_001_fold4

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: 0.2186
  • Accuracy: 0.9286

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
  • 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
1.2742 1.0 219 1.3421 0.4286
1.1364 2.0 438 1.2693 0.4048
0.9912 3.0 657 1.1701 0.5238
0.8292 4.0 876 1.0493 0.6429
0.7771 5.0 1095 0.9148 0.7143
0.6956 6.0 1314 0.8048 0.7381
0.519 7.0 1533 0.7062 0.8095
0.5042 8.0 1752 0.6401 0.7857
0.4397 9.0 1971 0.5785 0.8333
0.3933 10.0 2190 0.5338 0.8571
0.341 11.0 2409 0.4959 0.8810
0.3345 12.0 2628 0.4569 0.8810
0.2949 13.0 2847 0.4265 0.9048
0.2608 14.0 3066 0.3999 0.9286
0.2368 15.0 3285 0.3796 0.9286
0.2257 16.0 3504 0.3614 0.9286
0.232 17.0 3723 0.3430 0.9286
0.1928 18.0 3942 0.3249 0.9286
0.1804 19.0 4161 0.3144 0.9286
0.1542 20.0 4380 0.3019 0.9048
0.1333 21.0 4599 0.2915 0.9286
0.1333 22.0 4818 0.2894 0.9048
0.1178 23.0 5037 0.2746 0.9286
0.1098 24.0 5256 0.2771 0.9048
0.1099 25.0 5475 0.2649 0.9048
0.0836 26.0 5694 0.2732 0.9048
0.0751 27.0 5913 0.2625 0.9048
0.0745 28.0 6132 0.2608 0.9048
0.0826 29.0 6351 0.2526 0.9048
0.079 30.0 6570 0.2463 0.9286
0.0659 31.0 6789 0.2439 0.9048
0.0738 32.0 7008 0.2422 0.9286
0.0683 33.0 7227 0.2335 0.9286
0.0674 34.0 7446 0.2343 0.9048
0.0633 35.0 7665 0.2311 0.9048
0.0608 36.0 7884 0.2259 0.9286
0.0543 37.0 8103 0.2239 0.9286
0.0444 38.0 8322 0.2256 0.9286
0.0496 39.0 8541 0.2255 0.9286
0.0513 40.0 8760 0.2253 0.9286
0.0449 41.0 8979 0.2226 0.9286
0.0449 42.0 9198 0.2216 0.9286
0.0549 43.0 9417 0.2202 0.9286
0.0488 44.0 9636 0.2213 0.9286
0.0437 45.0 9855 0.2208 0.9286
0.0362 46.0 10074 0.2201 0.9286
0.0622 47.0 10293 0.2188 0.9286
0.0546 48.0 10512 0.2185 0.9286
0.0472 49.0 10731 0.2186 0.9286
0.0581 50.0 10950 0.2186 0.9286

Framework versions

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