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van-base-finetuned-eurosat-imgaug

This model is a fine-tuned version of Visual-Attention-Network/van-base on the image_folder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0379
  • Accuracy: 0.9885

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: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.0887 1.0 190 0.0589 0.98
0.055 2.0 380 0.0390 0.9878
0.0223 3.0 570 0.0379 0.9885

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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Evaluation results