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segformer-b0-finetuned-segments-stamp-verification

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segformer-b0-finetuned-segments-stamp-verification / README.md
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metadata
license: other
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-segments-stamp-verification
    results: []

segformer-b0-finetuned-segments-stamp-verification

This model is a fine-tuned version of nvidia/mit-b0 on the bilal01/stamp-verification dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0372
  • Mean Iou: 0.1908
  • Mean Accuracy: 0.3817
  • Overall Accuracy: 0.3817
  • Accuracy Unlabeled: nan
  • Accuracy Stamp: 0.3817
  • Iou Unlabeled: 0.0
  • Iou Stamp: 0.3817

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: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Stamp Iou Unlabeled Iou Stamp
0.3384 0.83 20 0.2769 0.0335 0.0670 0.0670 nan 0.0670 0.0 0.0670
0.2626 1.67 40 0.2201 0.1256 0.2512 0.2512 nan 0.2512 0.0 0.2512
0.1944 2.5 60 0.1918 0.2030 0.4060 0.4060 nan 0.4060 0.0 0.4060
0.2665 3.33 80 0.1564 0.1574 0.3148 0.3148 nan 0.3148 0.0 0.3148
0.1351 4.17 100 0.1194 0.1817 0.3634 0.3634 nan 0.3634 0.0 0.3634
0.1156 5.0 120 0.1035 0.1334 0.2668 0.2668 nan 0.2668 0.0 0.2668
0.1103 5.83 140 0.0895 0.1819 0.3638 0.3638 nan 0.3638 0.0 0.3638
0.0882 6.67 160 0.0746 0.0833 0.1665 0.1665 nan 0.1665 0.0 0.1665
0.0778 7.5 180 0.0655 0.1927 0.3854 0.3854 nan 0.3854 0.0 0.3854
0.0672 8.33 200 0.0585 0.1327 0.2654 0.2654 nan 0.2654 0.0 0.2654
0.0612 9.17 220 0.0615 0.1640 0.3279 0.3279 nan 0.3279 0.0 0.3279
0.0611 10.0 240 0.0546 0.2466 0.4933 0.4933 nan 0.4933 0.0 0.4933
0.0537 10.83 260 0.0499 0.1129 0.2258 0.2258 nan 0.2258 0.0 0.2258
0.0504 11.67 280 0.0502 0.1857 0.3713 0.3713 nan 0.3713 0.0 0.3713
0.0707 12.5 300 0.0442 0.1710 0.3419 0.3419 nan 0.3419 0.0 0.3419
0.0508 13.33 320 0.0434 0.2003 0.4006 0.4006 nan 0.4006 0.0 0.4006
0.0396 14.17 340 0.0420 0.1409 0.2818 0.2818 nan 0.2818 0.0 0.2818
0.0395 15.0 360 0.0417 0.1640 0.3280 0.3280 nan 0.3280 0.0 0.3280
0.0387 15.83 380 0.0397 0.1827 0.3655 0.3655 nan 0.3655 0.0 0.3655
0.0458 16.67 400 0.0387 0.1582 0.3165 0.3165 nan 0.3165 0.0 0.3165
0.0363 17.5 420 0.0390 0.1724 0.3449 0.3449 nan 0.3449 0.0 0.3449
0.0401 18.33 440 0.0382 0.2018 0.4036 0.4036 nan 0.4036 0.0 0.4036
0.0355 19.17 460 0.0382 0.2032 0.4064 0.4064 nan 0.4064 0.0 0.4064
0.0447 20.0 480 0.0372 0.1908 0.3817 0.3817 nan 0.3817 0.0 0.3817

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.0+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3