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vc-bantai-vit-withoutAMBI-adunest-v2

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: 0.8271
  • Accuracy: 0.7705

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 0.4 100 0.3811 0.8511
No log 0.81 200 0.3707 0.8609
No log 1.21 300 0.5708 0.7325
No log 1.61 400 0.3121 0.8778
0.3308 2.02 500 0.3358 0.8445
0.3308 2.42 600 0.2820 0.8768
0.3308 2.82 700 0.4825 0.7695
0.3308 3.23 800 0.3133 0.8640
0.3308 3.63 900 0.4509 0.8219
0.2028 4.03 1000 0.5426 0.7551
0.2028 4.44 1100 0.4886 0.8552
0.2028 4.84 1200 0.5649 0.7695
0.2028 5.24 1300 0.5925 0.7900
0.2028 5.65 1400 0.4203 0.8439
0.1471 6.05 1500 0.4275 0.8486
0.1471 6.45 1600 0.3683 0.8727
0.1471 6.85 1700 0.5709 0.8121
0.1471 7.26 1800 0.6209 0.7680
0.1471 7.66 1900 0.4971 0.8147
0.101 8.06 2000 0.8792 0.7567
0.101 8.47 2100 0.3288 0.8670
0.101 8.87 2200 0.3643 0.8342
0.101 9.27 2300 0.4883 0.8711
0.101 9.68 2400 0.2892 0.8943
0.0667 10.08 2500 0.5437 0.8398
0.0667 10.48 2600 0.5841 0.8450
0.0667 10.89 2700 0.8016 0.8219
0.0667 11.29 2800 0.6389 0.7772
0.0667 11.69 2900 0.3714 0.8753
0.0674 12.1 3000 0.9811 0.7130
0.0674 12.5 3100 0.6359 0.8101
0.0674 12.9 3200 0.5691 0.8285
0.0674 13.31 3300 0.6123 0.8316
0.0674 13.71 3400 0.3655 0.8978
0.0525 14.11 3500 0.4988 0.8583
0.0525 14.52 3600 0.6153 0.8450
0.0525 14.92 3700 0.4189 0.8881
0.0525 15.32 3800 0.9713 0.7967
0.0525 15.73 3900 1.1224 0.7967
0.0438 16.13 4000 0.5725 0.8578
0.0438 16.53 4100 0.4725 0.8532
0.0438 16.94 4200 0.4696 0.8640
0.0438 17.34 4300 0.4028 0.8789
0.0438 17.74 4400 0.9452 0.7746
0.0462 18.15 4500 0.4455 0.8783
0.0462 18.55 4600 0.6328 0.8311
0.0462 18.95 4700 0.6707 0.8296
0.0462 19.35 4800 0.7771 0.8429
0.0462 19.76 4900 1.2832 0.7408
0.0381 20.16 5000 0.5415 0.8737
0.0381 20.56 5100 0.8932 0.7977
0.0381 20.97 5200 0.5182 0.8691
0.0381 21.37 5300 0.5967 0.8794
0.0381 21.77 5400 0.8271 0.7705

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