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binarization-segformer-b3

Image Segmentation
Transformers
PyTorch
Safetensors
segformer
Generated from Trainer
document-image-binarization
Inference Endpoints
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binarization-segformer-b3 / README.md
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metadata
license: openrail
tags:
  - document-image-binarization
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: binarization-segformer-b3
    results: []
pipeline_tag: image-segmentation

binarization-segformer-b3

This model is a fine-tuned version of nvidia/segformer-b3 on the same ensemble of 13 datasets as the SauvolaNet work publicly available in their GitHub repository.

It achieves the following results on the evaluation set on DIBCO metrics:

  • loss: 0.1017
  • F-measure: 0.9776
  • pseudo F-measure: 0.9531
  • PSNR: 14.5040
  • DRD: 5.3749

with PSNR the peak signal-to-noise ratio and DRD the distance reciprocal distortion.

For more information on the above DIBCO metrics, see the 2017 introductory paper.

Warning: This model only accepts images with a resolution of 640 due to GPU compute constraints on Colab free tier during training.

Model description

This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO). This is in contrast to the late trend of adapting classic binarization algorithms with neural networks, such as DeepOtsu or the aforementioned SauvolaNet work as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 10
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 50

Training results

training loss epoch step validation loss F-measure pseudo F-measure PSNR DRD
0.6667 1.03 10 0.6683 0.7127 0.6831 4.8248 107.2894
0.6371 2.05 20 0.6390 0.8173 0.7360 6.1079 69.7770
0.587 3.08 30 0.5652 0.8934 0.8187 7.9143 40.5464
0.5288 4.1 40 0.4926 0.9240 0.8554 9.2247 27.4220
0.4601 5.13 50 0.4244 0.9490 0.8944 10.8830 16.8051
0.3864 6.15 60 0.3446 0.9638 0.9218 12.3460 10.6997
0.3331 7.18 70 0.3055 0.9693 0.9317 13.0531 8.5298
0.2821 8.21 80 0.2512 0.9736 0.9427 13.6929 6.8343
0.2392 9.23 90 0.2112 0.9744 0.9462 13.8825 6.4094
0.2126 10.26 100 0.1948 0.9743 0.9433 13.8424 6.5637
0.1889 11.28 110 0.1710 0.9749 0.9499 13.9784 6.1757
0.1662 12.31 120 0.1604 0.9753 0.9495 14.0450 6.0929
0.1506 13.33 130 0.1451 0.9750 0.9550 14.0028 6.1031
0.1359 14.36 140 0.1362 0.9759 0.9501 14.1383 5.9699
0.1321 15.38 150 0.1351 0.9761 0.9485 14.1907 5.9045
0.1283 16.41 160 0.1266 0.9758 0.9541 14.1515 5.8287
0.1198 17.44 170 0.1232 0.9763 0.9535 14.2411 5.7300
0.1151 18.46 180 0.1232 0.9765 0.9482 14.2788 5.8266
0.1146 19.49 190 0.1183 0.9764 0.9530 14.2363 5.7922
0.1027 20.51 200 0.1162 0.9765 0.9535 14.2867 5.6246
0.1051 21.54 210 0.1146 0.9766 0.9551 14.2963 5.6159
0.1095 22.56 220 0.1159 0.9767 0.9497 14.3153 5.8966
0.1076 23.59 230 0.1106 0.9768 0.9533 14.3267 5.6436
0.1006 24.62 240 0.1113 0.9769 0.9483 14.3683 5.6679
0.1077 25.64 250 0.1086 0.9770 0.9544 14.3843 5.4949
0.0966 26.67 260 0.1077 0.9770 0.9553 14.3660 5.5337
0.0958 27.69 270 0.1071 0.9773 0.9529 14.4405 5.4582
0.0984 28.72 280 0.1055 0.9772 0.9536 14.4405 5.4365
0.0936 29.74 290 0.1056 0.9774 0.9528 14.4634 5.4066
0.0958 30.77 300 0.1049 0.9772 0.9544 14.4138 5.4854
0.0896 31.79 310 0.1043 0.9774 0.9533 14.4593 5.4351
0.0973 32.82 320 0.1035 0.9774 0.9528 14.4633 5.4430
0.0943 33.85 330 0.1033 0.9775 0.9527 14.4809 5.4193
0.0956 34.87 340 0.1026 0.9774 0.9543 14.4576 5.4070
0.0936 35.9 350 0.1031 0.9775 0.9531 14.4827 5.4137
0.0937 36.92 360 0.1028 0.9773 0.9551 14.4420 5.4084
0.0952 37.95 370 0.1023 0.9775 0.9541 14.4809 5.3769
0.0952 38.97 380 0.1023 0.9776 0.9525 14.5086 5.3839
0.0948 40.0 390 0.1020 0.9774 0.9546 14.4667 5.3800
0.0931 41.03 400 0.1020 0.9776 0.9534 14.5043 5.3728
0.0906 42.05 410 0.1023 0.9774 0.9544 14.4771 5.3773
0.0974 43.08 420 0.1019 0.9776 0.9536 14.5024 5.3718
0.0908 44.1 430 0.1025 0.9776 0.9536 14.4995 5.3730
0.0935 45.13 440 0.1024 0.9775 0.9537 14.4978 5.3715
0.0927 46.15 450 0.1017 0.9776 0.9531 14.5040 5.3749

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3