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hushem_40x_deit_small_sgd_001_fold2

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: hushem_40x_deit_small_sgd_001_fold2
    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: 0.7333333333333333

hushem_40x_deit_small_sgd_001_fold2

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: 0.9318
  • Accuracy: 0.7333

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
1.1891 1.0 215 1.3300 0.3778
0.9647 2.0 430 1.2794 0.4444
0.8581 3.0 645 1.2244 0.5111
0.699 4.0 860 1.1784 0.5333
0.6158 5.0 1075 1.1498 0.5111
0.5391 6.0 1290 1.1059 0.5556
0.4953 7.0 1505 1.0650 0.5333
0.4016 8.0 1720 1.0249 0.5556
0.3397 9.0 1935 0.9796 0.6222
0.3003 10.0 2150 0.9463 0.7111
0.246 11.0 2365 0.9270 0.7111
0.1949 12.0 2580 0.9025 0.7111
0.1895 13.0 2795 0.8872 0.7111
0.1659 14.0 3010 0.8723 0.7111
0.1576 15.0 3225 0.8544 0.7111
0.1305 16.0 3440 0.8521 0.7111
0.1123 17.0 3655 0.8414 0.7111
0.1025 18.0 3870 0.8453 0.7111
0.0749 19.0 4085 0.8597 0.7111
0.0854 20.0 4300 0.8467 0.7111
0.0788 21.0 4515 0.8314 0.7111
0.0675 22.0 4730 0.8392 0.7111
0.0523 23.0 4945 0.8293 0.7111
0.0556 24.0 5160 0.8555 0.7111
0.0483 25.0 5375 0.8566 0.7111
0.0417 26.0 5590 0.8533 0.7111
0.0397 27.0 5805 0.8560 0.7333
0.0302 28.0 6020 0.8587 0.7333
0.0286 29.0 6235 0.8633 0.7333
0.0386 30.0 6450 0.8691 0.7333
0.0212 31.0 6665 0.8693 0.7333
0.0221 32.0 6880 0.8714 0.7333
0.0198 33.0 7095 0.8818 0.7333
0.0189 34.0 7310 0.8880 0.7333
0.0167 35.0 7525 0.8939 0.7333
0.0198 36.0 7740 0.9010 0.7333
0.0157 37.0 7955 0.8988 0.7333
0.0177 38.0 8170 0.9154 0.7333
0.0136 39.0 8385 0.9094 0.7333
0.0108 40.0 8600 0.9213 0.7333
0.0119 41.0 8815 0.9173 0.7333
0.0127 42.0 9030 0.9219 0.7333
0.0095 43.0 9245 0.9256 0.7333
0.0124 44.0 9460 0.9223 0.7333
0.0112 45.0 9675 0.9246 0.7333
0.0112 46.0 9890 0.9266 0.7333
0.0102 47.0 10105 0.9301 0.7333
0.0105 48.0 10320 0.9338 0.7333
0.0119 49.0 10535 0.9314 0.7333
0.0144 50.0 10750 0.9318 0.7333

Framework versions

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