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vit-base-patch16-224-ve-U13b-80RX1

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vit-base-patch16-224-ve-U13b-80RX1

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

  • Loss: 0.7770
  • Accuracy: 0.8478

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: 5.5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.3157 0.99 51 1.2968 0.3478
1.0334 2.0 103 1.0060 0.5217
0.691 2.99 154 0.7506 0.7609
0.5005 4.0 206 0.6433 0.7826
0.3478 4.99 257 0.5674 0.7609
0.3339 6.0 309 0.6623 0.7609
0.2533 6.99 360 0.6905 0.7391
0.138 8.0 412 0.7251 0.7826
0.1289 8.99 463 0.7467 0.7391
0.152 10.0 515 0.9011 0.7174
0.2609 10.99 566 1.0150 0.7174
0.2202 12.0 618 0.9713 0.7826
0.1083 12.99 669 1.1106 0.6739
0.07 14.0 721 1.1211 0.7174
0.0791 14.99 772 1.1830 0.7609
0.0427 16.0 824 0.7770 0.8478
0.1219 16.99 875 1.0962 0.7391
0.0739 18.0 927 0.9447 0.7609
0.1989 18.99 978 1.1543 0.7391
0.1097 20.0 1030 1.1795 0.7609
0.1204 20.99 1081 1.2679 0.6739
0.0514 22.0 1133 1.0646 0.7174
0.0612 22.99 1184 1.1413 0.6957
0.0207 24.0 1236 0.8928 0.7826
0.1063 24.99 1287 1.1186 0.7609
0.1076 26.0 1339 1.1741 0.7609
0.0714 26.99 1390 1.0977 0.8043
0.062 28.0 1442 1.3965 0.7174
0.0617 28.99 1493 1.1849 0.7609
0.0536 30.0 1545 1.0865 0.7826
0.0707 30.99 1596 1.2081 0.7609
0.0967 32.0 1648 1.3300 0.7391
0.0564 32.99 1699 1.2240 0.7826
0.0435 34.0 1751 1.2391 0.7609
0.043 34.99 1802 1.1813 0.7609
0.0218 36.0 1854 1.2496 0.7826
0.0043 36.99 1905 1.2797 0.7174
0.0051 38.0 1957 1.2493 0.7391
0.0123 38.99 2008 1.2538 0.7391
0.0546 39.61 2040 1.2530 0.7609

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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Evaluation results

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