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metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-vit0
    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: 0.8199233716475096

swin-tiny-patch4-window7-224-vit0

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4985
  • Accuracy: 0.8199

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: 5e-05
  • 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: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.13 0.97 18 1.0297 0.4330
0.9066 2.0 37 0.8349 0.6590
0.7157 2.97 55 0.8050 0.6743
0.6446 4.0 74 0.6934 0.7165
0.5707 4.97 92 0.6324 0.7433
0.5042 6.0 111 0.6156 0.7356
0.4714 6.97 129 0.6825 0.7241
0.4225 8.0 148 0.5692 0.7625
0.3912 8.97 166 0.6150 0.7586
0.3442 10.0 185 0.4901 0.8008
0.289 10.97 203 0.5580 0.7739
0.2827 12.0 222 0.5308 0.7969
0.2375 12.97 240 0.5274 0.8046
0.2493 14.0 259 0.5433 0.8046
0.2309 14.97 277 0.5355 0.7931
0.1963 16.0 296 0.4836 0.8314
0.2162 16.97 314 0.4973 0.8238
0.2256 18.0 333 0.4918 0.8276
0.2124 18.97 351 0.5071 0.8161
0.1797 19.46 360 0.4985 0.8199

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0