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Electrcical-IMAGE-finetuned-eurosat

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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: Electrcical-IMAGE-finetuned-eurosat
    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: 0.8960396039603961

Electrcical-IMAGE-finetuned-eurosat

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: 0.3583
  • Accuracy: 0.8960

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
1.6143 0.98 28 1.2882 0.5347
0.8597 2.0 57 0.7302 0.7649
0.5858 2.98 85 0.4849 0.8465
0.4332 4.0 114 0.4274 0.8614
0.4054 4.98 142 0.3687 0.8787
0.3826 6.0 171 0.3788 0.8614
0.3561 6.98 199 0.3700 0.8936
0.2838 8.0 228 0.3550 0.8812
0.2897 8.98 256 0.3698 0.8886
0.2519 10.0 285 0.3459 0.8837
0.2194 10.98 313 0.3583 0.8960
0.1955 12.0 342 0.3442 0.8886
0.2443 12.98 370 0.3801 0.8787
0.207 14.0 399 0.3499 0.8861
0.2078 14.98 427 0.3701 0.8837
0.1873 16.0 456 0.3773 0.8861
0.1697 16.98 484 0.3753 0.8861
0.1812 18.0 513 0.3747 0.8911
0.151 18.98 541 0.3736 0.8861
0.1567 19.65 560 0.3726 0.8861

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2