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vc-bantai-vit-withoutAMBI-adunest-v3

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

  • Loss: 0.8889
  • Accuracy: 0.8218

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: 0.0005
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.38 100 0.8208 0.7147
No log 0.76 200 0.8861 0.7595
No log 1.14 300 0.4306 0.7910
No log 1.52 400 0.5222 0.8245
0.3448 1.9 500 0.8621 0.7602
0.3448 2.28 600 0.2902 0.8801
0.3448 2.66 700 0.3687 0.8426
0.3448 3.04 800 0.3585 0.8694
0.3448 3.42 900 0.6546 0.7897
0.2183 3.8 1000 0.3881 0.8272
0.2183 4.18 1100 0.9650 0.7709
0.2183 4.56 1200 0.6444 0.7917
0.2183 4.94 1300 0.4685 0.8707
0.2183 5.32 1400 0.4972 0.8506
0.157 5.7 1500 0.4010 0.8513
0.157 6.08 1600 0.4629 0.8419
0.157 6.46 1700 0.4258 0.8714
0.157 6.84 1800 0.4383 0.8573
0.157 7.22 1900 0.5324 0.8493
0.113 7.6 2000 0.3212 0.8942
0.113 7.98 2100 0.8621 0.8326
0.113 8.37 2200 0.6050 0.8131
0.113 8.75 2300 0.7173 0.7991
0.113 9.13 2400 0.5313 0.8125
0.0921 9.51 2500 0.6584 0.8158
0.0921 9.89 2600 0.8727 0.7930
0.0921 10.27 2700 0.4222 0.8922
0.0921 10.65 2800 0.5811 0.8265
0.0921 11.03 2900 0.6175 0.8372
0.0701 11.41 3000 0.3914 0.8835
0.0701 11.79 3100 0.3364 0.8654
0.0701 12.17 3200 0.6223 0.8359
0.0701 12.55 3300 0.7830 0.8125
0.0701 12.93 3400 0.4356 0.8942
0.0552 13.31 3500 0.7553 0.8232
0.0552 13.69 3600 0.9107 0.8292
0.0552 14.07 3700 0.6108 0.8580
0.0552 14.45 3800 0.5732 0.8567
0.0552 14.83 3900 0.5087 0.8614
0.0482 15.21 4000 0.8889 0.8218

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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Evaluation results