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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: 0.8787128712871287

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: 0.3505
  • Accuracy: 0.8787

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
1.5532 0.98 28 1.1704 0.6163
0.8115 2.0 57 0.6827 0.7673
0.5513 2.98 85 0.4525 0.8416
0.455 4.0 114 0.4012 0.8540
0.3901 4.98 142 0.3824 0.8614
0.4042 6.0 171 0.3797 0.8639
0.3591 6.98 199 0.3505 0.8787
0.2989 8.0 228 0.3551 0.8614
0.3029 8.98 256 0.3625 0.8663
0.2606 10.0 285 0.3615 0.8490
0.2413 10.98 313 0.3435 0.8787
0.2051 12.0 342 0.3371 0.8663
0.2477 12.98 370 0.3451 0.8639
0.2271 14.0 399 0.3364 0.8738
0.2112 14.98 427 0.3559 0.8639
0.1902 16.0 456 0.3630 0.8738
0.1739 16.98 484 0.3630 0.8713
0.195 18.0 513 0.3625 0.8663
0.1621 18.98 541 0.3571 0.8762
0.154 19.65 560 0.3555 0.8738

Framework versions

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