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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-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

swin-tiny-patch4-window7-224-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: 5.5031
  • Accuracy: 0.0

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: 256
  • eval_batch_size: 256
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 1024
  • 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
No log 1.0 1 0.6744 1.0
No log 2.0 2 0.7507 0.0
No log 3.0 3 0.9175 0.0
No log 4.0 4 1.1669 0.0
No log 5.0 5 1.4443 0.0
No log 6.0 6 1.7218 0.0
No log 7.0 7 2.0269 0.0
No log 8.0 8 2.3374 0.0
No log 9.0 9 2.6657 0.0
0.0781 10.0 10 2.9900 0.0
0.0781 11.0 11 3.2990 0.0
0.0781 12.0 12 3.5921 0.0
0.0781 13.0 13 3.8577 0.0
0.0781 14.0 14 4.1048 0.0
0.0781 15.0 15 4.3232 0.0
0.0781 16.0 16 4.5163 0.0
0.0781 17.0 17 4.6854 0.0
0.0781 18.0 18 4.8332 0.0
0.0781 19.0 19 4.9602 0.0
0.0003 20.0 20 5.0735 0.0
0.0003 21.0 21 5.1691 0.0
0.0003 22.0 22 5.2486 0.0
0.0003 23.0 23 5.3151 0.0
0.0003 24.0 24 5.3696 0.0
0.0003 25.0 25 5.4131 0.0
0.0003 26.0 26 5.4466 0.0
0.0003 27.0 27 5.4711 0.0
0.0003 28.0 28 5.4879 0.0
0.0003 29.0 29 5.4983 0.0
0.0 30.0 30 5.5031 0.0

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.13.3