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violation-classification-bantai-vit-v80ep

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image_folder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1974
  • Accuracy: 0.9560

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: 80

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.797 4.95 500 0.3926 0.8715
0.3095 9.9 1000 0.2597 0.9107
0.1726 14.85 1500 0.2157 0.9253
0.1259 19.8 2000 0.1870 0.9392
0.0959 24.75 2500 0.1797 0.9444
0.0835 29.7 3000 0.2293 0.9354
0.0722 34.65 3500 0.1921 0.9441
0.0628 39.6 4000 0.1897 0.9491
0.059 44.55 4500 0.1719 0.9520
0.0531 49.5 5000 0.1987 0.9513
0.046 54.45 5500 0.1713 0.9556
0.0444 59.4 6000 0.2016 0.9525
0.042 64.36 6500 0.1950 0.9525
0.0363 69.31 7000 0.2017 0.9549
0.037 74.26 7500 0.1943 0.9551
0.0343 79.21 8000 0.1974 0.9560

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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Evaluation results