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metadata
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: 2024_08_16_swinv2-base-patch4-window8-256
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8068181818181818

2024_08_16_swinv2-base-patch4-window8-256

This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4529
  • Accuracy: 0.8068

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: 1e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6942 1.0 87 0.6796 0.6818
0.6661 2.0 174 0.5973 0.7045
0.5666 3.0 261 0.5844 0.7045
0.6134 4.0 348 0.5798 0.7045
0.5207 5.0 435 0.5817 0.6932
0.75 6.0 522 0.5488 0.7159
0.4155 7.0 609 0.5373 0.7045
0.5122 8.0 696 0.5057 0.7386
0.722 9.0 783 0.4951 0.7045
0.5301 10.0 870 0.5349 0.7727
0.5881 11.0 957 0.4795 0.7955
0.5295 12.0 1044 0.4843 0.7955
0.6252 13.0 1131 0.4529 0.8068
0.9347 14.0 1218 0.4670 0.7955
0.5375 15.0 1305 0.4468 0.8068
0.4811 16.0 1392 0.4912 0.7841
0.5728 17.0 1479 0.4636 0.8068
0.7997 18.0 1566 0.4631 0.8068
0.4473 19.0 1653 0.4785 0.8068
0.4999 20.0 1740 0.5162 0.8068
0.4572 21.0 1827 0.5742 0.7955
0.2571 22.0 1914 0.5181 0.7955
0.5085 23.0 2001 0.4937 0.7955
0.7698 24.0 2088 0.4764 0.7955
0.558 25.0 2175 0.4742 0.8068
0.5462 26.0 2262 0.5320 0.7841
0.5218 27.0 2349 0.5298 0.7841
0.5228 28.0 2436 0.5182 0.7955
0.5787 29.0 2523 0.5104 0.8068
0.7511 30.0 2610 0.5152 0.8068

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.3.1+cu118
  • Datasets 2.20.0
  • Tokenizers 0.19.1