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vit-base-cifar10

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-cifar10
    results: []

vit-base-cifar10

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

  • Loss: 0.0803
  • Accuracy: 0.9773

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.1043 0.0457 100 0.2855 0.919
0.2671 0.0914 200 0.3650 0.9015
0.2935 0.1371 300 0.3167 0.9067
0.27 0.1828 400 0.3518 0.8922
0.3634 0.2285 500 0.3660 0.8953
0.2559 0.2742 600 0.3964 0.8901
0.197 0.3199 700 0.2481 0.9253
0.2594 0.3656 800 0.2486 0.923
0.4545 0.4113 900 0.3271 0.9
0.1243 0.4570 1000 0.2448 0.9269
0.3593 0.5027 1100 0.2118 0.9354
0.1375 0.5484 1200 0.2205 0.9349
0.1521 0.5941 1300 0.2009 0.9376
0.1237 0.6399 1400 0.1803 0.9445
0.2214 0.6856 1500 0.2026 0.9395
0.1324 0.7313 1600 0.1635 0.9493
0.1864 0.7770 1700 0.1672 0.9493
0.128 0.8227 1800 0.2015 0.9409
0.121 0.8684 1900 0.1753 0.9451
0.1918 0.9141 2000 0.1370 0.9588
0.1658 0.9598 2100 0.1543 0.9535
0.1088 1.0055 2200 0.1361 0.9577
0.0916 1.0512 2300 0.1393 0.9597
0.005 1.0969 2400 0.1295 0.9621
0.0294 1.1426 2500 0.1327 0.9639
0.0939 1.1883 2600 0.1409 0.9621
0.0756 1.2340 2700 0.1202 0.9682
0.0466 1.2797 2800 0.1274 0.964
0.0565 1.3254 2900 0.1250 0.9663
0.0609 1.3711 3000 0.1299 0.9657
0.0201 1.4168 3100 0.1203 0.9685
0.0258 1.4625 3200 0.1166 0.9693
0.0913 1.5082 3300 0.1009 0.9736
0.0235 1.5539 3400 0.0964 0.9732
0.0089 1.5996 3500 0.0966 0.9747
0.0455 1.6453 3600 0.0963 0.9748
0.0271 1.6910 3700 0.0874 0.9763
0.0407 1.7367 3800 0.0898 0.9761
0.1095 1.7824 3900 0.0849 0.976
0.0327 1.8282 4000 0.0926 0.9745
0.0427 1.8739 4100 0.0811 0.9769
0.003 1.9196 4200 0.0821 0.9761
0.0182 1.9653 4300 0.0803 0.9773

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1