
# binarization-segformer-b3

This model is a fine-tuned version of [nvidia/segformer-b3-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024)
on the same ensemble of 13 datasets as the [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf) work publicly available 
in their GitHub [repository](https://github.com/Leedeng/SauvolaNet#datasets).

It achieves the following results on the evaluation set on DIBCO metrics:
- loss: 0.0743
- DRD: 5.9548
- F-measure: 0.9840
- pseudo F-measure: 0.9740
- PSNR: 16.0119

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](https://ieeexplore.ieee.org/document/8270159).

## 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 classical binarization algorithms with neural networks, 
such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf)
as extensions of Otsu's method and Sauvola thresholding algorithm, respectively.

## Intended uses & limitations

TBC

## Training and evaluation data

TBC

## Training procedure

### Training hyperparameters

TBC

### Training results

| training loss | epoch | step | validation loss | DRD      | F-measure | pseudo F-measure | PSNR    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:----------------:|:-------:|
| 0.6983        | 0.26  | 10   | 0.7079          | 199.5096 | 0.5945    | 0.5801           | 3.4552  |
| 0.6657        | 0.52  | 20   | 0.6755          | 149.2346 | 0.7006    | 0.6165           | 4.6752  |
| 0.6145        | 0.77  | 30   | 0.6433          | 109.7298 | 0.7831    | 0.6520           | 5.5489  |
| 0.5553        | 1.03  | 40   | 0.5443          | 53.7149  | 0.8952    | 0.8000           | 8.1736  |
| 0.4627        | 1.29  | 50   | 0.4896          | 32.7649  | 0.9321    | 0.8603           | 9.8706  |
| 0.3969        | 1.55  | 60   | 0.4327          | 21.5508  | 0.9526    | 0.8985           | 11.3400 |
| 0.3414        | 1.81  | 70   | 0.3002          | 11.0094  | 0.9732    | 0.9462           | 13.5901 |
| 0.2898        | 2.06  | 80   | 0.2839          | 10.1064  | 0.9748    | 0.9563           | 13.9796 |
| 0.2292        | 2.32  | 90   | 0.2427          | 9.4437   | 0.9761    | 0.9584           | 14.2161 |
| 0.2153        | 2.58  | 100  | 0.2095          | 8.8696   | 0.9771    | 0.9621           | 14.4319 |
| 0.1767        | 2.84  | 110  | 0.1916          | 8.6152   | 0.9776    | 0.9646           | 14.5528 |
| 0.1509        | 3.1   | 120  | 0.1704          | 8.0761   | 0.9791    | 0.9632           | 14.7961 |
| 0.1265        | 3.35  | 130  | 0.1561          | 8.5627   | 0.9784    | 0.9655           | 14.7400 |
| 0.132         | 3.61  | 140  | 0.1318          | 8.1849   | 0.9788    | 0.9670           | 14.8469 |
| 0.1115        | 3.87  | 150  | 0.1317          | 7.8438   | 0.9790    | 0.9657           | 14.9072 |
| 0.0983        | 4.13  | 160  | 0.1273          | 7.9405   | 0.9791    | 0.9673           | 14.9701 |
| 0.1001        | 4.39  | 170  | 0.1234          | 8.4132   | 0.9788    | 0.9691           | 14.8573 |
| 0.0862        | 4.65  | 180  | 0.1147          | 8.0838   | 0.9797    | 0.9678           | 15.0433 |
| 0.0713        | 4.9   | 190  | 0.1134          | 7.6027   | 0.9806    | 0.9687           | 15.2235 |
| 0.0905        | 5.16  | 200  | 0.1061          | 7.2973   | 0.9803    | 0.9699           | 15.1646 |
| 0.0902        | 5.42  | 210  | 0.1061          | 8.4049   | 0.9787    | 0.9699           | 14.8460 |
| 0.0759        | 5.68  | 220  | 0.1062          | 7.7147   | 0.9809    | 0.9695           | 15.2426 |
| 0.0638        | 5.94  | 230  | 0.1019          | 7.7449   | 0.9806    | 0.9695           | 15.2195 |
| 0.0852        | 6.19  | 240  | 0.0962          | 7.0221   | 0.9817    | 0.9693           | 15.4730 |
| 0.0677        | 6.45  | 250  | 0.0961          | 7.2520   | 0.9814    | 0.9710           | 15.3878 |
| 0.0668        | 6.71  | 260  | 0.0972          | 6.6658   | 0.9823    | 0.9689           | 15.6106 |
| 0.0701        | 6.97  | 270  | 0.0909          | 6.9454   | 0.9820    | 0.9713           | 15.5458 |
| 0.0567        | 7.23  | 280  | 0.0925          | 6.5498   | 0.9824    | 0.9718           | 15.5965 |
| 0.0624        | 7.48  | 290  | 0.0899          | 7.3125   | 0.9813    | 0.9717           | 15.3255 |
| 0.0649        | 7.74  | 300  | 0.0932          | 7.4915   | 0.9816    | 0.9684           | 15.5666 |
| 0.0524        | 8.0   | 310  | 0.0905          | 7.1666   | 0.9815    | 0.9711           | 15.4526 |
| 0.0693        | 8.26  | 320  | 0.0901          | 6.5627   | 0.9827    | 0.9704           | 15.7335 |
| 0.0528        | 8.52  | 330  | 0.0845          | 6.6690   | 0.9826    | 0.9734           | 15.5950 |
| 0.0632        | 8.77  | 340  | 0.0822          | 6.2661   | 0.9833    | 0.9723           | 15.8631 |
| 0.0522        | 9.03  | 350  | 0.0844          | 6.0073   | 0.9836    | 0.9715           | 15.9393 |
| 0.0568        | 9.29  | 360  | 0.0817          | 5.9460   | 0.9837    | 0.9721           | 15.9523 |
| 0.057         | 9.55  | 370  | 0.0900          | 7.9726   | 0.9812    | 0.9730           | 15.1229 |
| 0.052         | 9.81  | 380  | 0.0836          | 6.5444   | 0.9822    | 0.9712           | 15.6388 |
| 0.0568        | 10.06 | 390  | 0.0810          | 6.0359   | 0.9836    | 0.9714           | 15.9796 |
| 0.0481        | 10.32 | 400  | 0.0784          | 6.2110   | 0.9835    | 0.9724           | 15.9235 |
| 0.0513        | 10.58 | 410  | 0.0803          | 6.0990   | 0.9835    | 0.9715           | 15.9502 |
| 0.0595        | 10.84 | 420  | 0.0798          | 6.0829   | 0.9835    | 0.9720           | 15.9052 |
| 0.047         | 11.1  | 430  | 0.0779          | 5.8847   | 0.9838    | 0.9725           | 16.0043 |
| 0.0406        | 11.35 | 440  | 0.0802          | 5.7944   | 0.9838    | 0.9713           | 16.0620 |
| 0.0493        | 11.61 | 450  | 0.0781          | 6.0947   | 0.9836    | 0.9731           | 15.9033 |
| 0.064         | 11.87 | 460  | 0.0769          | 6.1257   | 0.9837    | 0.9736           | 15.9080 |
| 0.0622        | 12.13 | 470  | 0.0765          | 6.2964   | 0.9835    | 0.9739           | 15.8188 |
| 0.0457        | 12.39 | 480  | 0.0773          | 5.9826   | 0.9838    | 0.9728           | 16.0119 |
| 0.0447        | 12.65 | 490  | 0.0761          | 5.7977   | 0.9841    | 0.9728           | 16.0900 |
| 0.0515        | 12.9  | 500  | 0.0750          | 5.8569   | 0.9840    | 0.9729           | 16.0633 |
| 0.0357        | 13.16 | 510  | 0.0796          | 5.7990   | 0.9837    | 0.9713           | 16.0818 |
| 0.0503        | 13.42 | 520  | 0.0749          | 5.8323   | 0.9841    | 0.9736           | 16.0510 |
| 0.0508        | 13.68 | 530  | 0.0746          | 6.0361   | 0.9839    | 0.9735           | 15.9709 |
| 0.0533        | 13.94 | 540  | 0.0768          | 6.1596   | 0.9836    | 0.9740           | 15.9193 |
| 0.0503        | 14.19 | 550  | 0.0739          | 5.5900   | 0.9843    | 0.9723           | 16.1883 |
| 0.0515        | 14.45 | 560  | 0.0740          | 5.4660   | 0.9845    | 0.9727           | 16.2745 |
| 0.0502        | 14.71 | 570  | 0.0740          | 5.5895   | 0.9844    | 0.9736           | 16.2054 |
| 0.0401        | 14.97 | 580  | 0.0741          | 5.9694   | 0.9840    | 0.9747           | 15.9603 |
| 0.0495        | 15.23 | 590  | 0.0745          | 5.9136   | 0.9841    | 0.9740           | 16.0458 |
| 0.0413        | 15.48 | 600  | 0.0743          | 5.9548   | 0.9840    | 0.9740           | 16.0119 |

### Framework versions

- transformers 4.31.0
- torch 2.0.0
- datasets 2.13.1
- tokenizers 0.13.3

{"license": "openrail", "tags": ["generated_from_trainer", "document-image-binarization", "image-segmentation"], "model-index": [{"name": "binarization-segformer-b3", "results": []}]}
transformers pytorch safetensors segformer generated_from_trainer document-image-binarization image-segmentation arxiv:2105.05521 arxiv:1901.06081 license:openrail endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #segformer #generated_from_trainer #document-image-binarization #image-segmentation #arxiv-2105.05521 #arxiv-1901.06081 #license-openrail #endpoints_compatible #region-us 

binarization-segformer-b3
=========================


This model is a fine-tuned version of nvidia/segformer-b3-1024-1024
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: 0.0743
* DRD: 5.9548
* F-measure: 0.9840
* pseudo F-measure: 0.9740
* PSNR: 16.0119


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.


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 classical binarization algorithms with neural networks,
such as DeepOtsu or SauvolaNet
as extensions of Otsu's method and Sauvola thresholding algorithm, respectively.


Intended uses & limitations
---------------------------


TBC


Training and evaluation data
----------------------------


TBC


Training procedure
------------------


### Training hyperparameters


TBC


### Training results



### Framework versions


* transformers 4.31.0
* torch 2.0.0
* datasets 2.13.1
* tokenizers 0.13.3



### Training hyperparameters


TBC ### Training results ### Framework versions


* transformers 4.31.0
* torch 2.0.0
* datasets 2.13.1
* tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #segformer #generated_from_trainer #document-image-binarization #image-segmentation #arxiv-2105.05521 #arxiv-1901.06081 #license-openrail #endpoints_compatible #region-us 
 ### Training hyperparameters


TBC ### Training results ### Framework versions


* transformers 4.31.0
* torch 2.0.0
* datasets 2.13.1
* tokenizers 0.13.3