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hushem_40x_deit_base_sgd_0001_fold4

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
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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_0001_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: 0.42857142857142855

hushem_40x_deit_base_sgd_0001_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: 1.2151
  • Accuracy: 0.4286

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
1.3918 1.0 219 1.4045 0.3095
1.3704 2.0 438 1.3956 0.3095
1.3491 3.0 657 1.3880 0.3333
1.3369 4.0 876 1.3811 0.3333
1.3406 5.0 1095 1.3747 0.3333
1.3171 6.0 1314 1.3686 0.3333
1.2982 7.0 1533 1.3628 0.3571
1.2896 8.0 1752 1.3571 0.3571
1.2549 9.0 1971 1.3513 0.3810
1.2384 10.0 2190 1.3457 0.4048
1.2507 11.0 2409 1.3401 0.4286
1.2362 12.0 2628 1.3346 0.4286
1.1966 13.0 2847 1.3293 0.4286
1.2279 14.0 3066 1.3240 0.4286
1.2136 15.0 3285 1.3188 0.4286
1.1856 16.0 3504 1.3138 0.4286
1.1941 17.0 3723 1.3088 0.4286
1.1805 18.0 3942 1.3039 0.4286
1.1554 19.0 4161 1.2991 0.4048
1.1709 20.0 4380 1.2943 0.4048
1.1523 21.0 4599 1.2895 0.4048
1.138 22.0 4818 1.2848 0.4048
1.0984 23.0 5037 1.2803 0.4048
1.1405 24.0 5256 1.2759 0.4048
1.1028 25.0 5475 1.2716 0.4286
1.1236 26.0 5694 1.2674 0.4286
1.0819 27.0 5913 1.2634 0.4286
1.1245 28.0 6132 1.2595 0.4286
1.0929 29.0 6351 1.2557 0.4286
1.0861 30.0 6570 1.2521 0.4048
1.082 31.0 6789 1.2486 0.4048
1.0826 32.0 7008 1.2452 0.4048
1.0889 33.0 7227 1.2420 0.4048
1.052 34.0 7446 1.2390 0.4286
1.056 35.0 7665 1.2361 0.4286
1.0391 36.0 7884 1.2333 0.4286
1.0236 37.0 8103 1.2307 0.4286
1.0474 38.0 8322 1.2283 0.4286
1.0069 39.0 8541 1.2261 0.4286
1.0443 40.0 8760 1.2242 0.4286
1.0711 41.0 8979 1.2223 0.4048
1.053 42.0 9198 1.2207 0.4286
1.0356 43.0 9417 1.2193 0.4286
1.0491 44.0 9636 1.2181 0.4286
0.9928 45.0 9855 1.2171 0.4286
1.0402 46.0 10074 1.2163 0.4286
1.0792 47.0 10293 1.2157 0.4286
1.0146 48.0 10512 1.2153 0.4286
1.0325 49.0 10731 1.2152 0.4286
1.0249 50.0 10950 1.2151 0.4286

Framework versions

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