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DiTo97
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binarization-segformer-b3

Image Segmentation
Transformers
PyTorch
Safetensors
segformer
Generated from Trainer
document-image-binarization
Inference Endpoints
Model card Files Community
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Removed old runs + old .safetensors + README.md

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README.md CHANGED
@@ -1,6 +1,5 @@
1
  ---
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- license: other
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- base_model: nvidia/segformer-b3-finetuned-cityscapes-1024-1024
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  tags:
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  - generated_from_trainer
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  - document-image-binarization
@@ -10,50 +9,47 @@ model-index:
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  results: []
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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  # binarization-segformer-b3
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- This model is a fine-tuned version of [nvidia/segformer-b3-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024) on the None dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.0743
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- - Drd: 5.9548
 
 
 
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  - F-measure: 0.9840
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- - Pseudo-f-measure: 0.9740
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- - Psnr: 16.0119
 
 
 
 
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  ## Model description
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- More information needed
 
 
 
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  ## Intended uses & limitations
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32
- More information needed
33
 
34
  ## Training and evaluation data
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36
- More information needed
37
 
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  ## Training procedure
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  ### Training hyperparameters
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- The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 4
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- - eval_batch_size: 4
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- - seed: 10
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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 16
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: cosine
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- - lr_scheduler_warmup_steps: 50
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- - num_epochs: 50
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Drd | F-measure | Pseudo-f-measure | Psnr |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:----------------:|:-------:|
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  | 0.6983 | 0.26 | 10 | 0.7079 | 199.5096 | 0.5945 | 0.5801 | 3.4552 |
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  | 0.6657 | 0.52 | 20 | 0.6755 | 149.2346 | 0.7006 | 0.6165 | 4.6752 |
@@ -116,10 +112,9 @@ The following hyperparameters were used during training:
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  | 0.0495 | 15.23 | 590 | 0.0745 | 5.9136 | 0.9841 | 0.9740 | 16.0458 |
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  | 0.0413 | 15.48 | 600 | 0.0743 | 5.9548 | 0.9840 | 0.9740 | 16.0119 |
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-
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  ### Framework versions
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- - Transformers 4.31.0
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- - Pytorch 2.0.0
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- - Datasets 2.13.1
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- - Tokenizers 0.13.3
 
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  ---
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+ license: openrail
 
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  tags:
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  - generated_from_trainer
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  - document-image-binarization
 
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  results: []
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  ---
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  # binarization-segformer-b3
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+ This model is a fine-tuned version of [nvidia/segformer-b3-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024)
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+ on the same ensemble of 13 datasets as the [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf) work publicly available
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+ in their GitHub [repository](https://github.com/Leedeng/SauvolaNet#datasets).
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+
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+ It achieves the following results on the evaluation set on DIBCO metrics:
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+ - loss: 0.0743
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+ - DRD: 5.9548
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  - F-measure: 0.9840
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+ - pseudo F-measure: 0.9740
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+ - PSNR: 16.0119
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+
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+ with PSNR the peak signal-to-noise ratio and DRD the distance reciprocal distortion.
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+
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+ For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
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  ## Model description
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+ This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
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+ This is in contrast to the late trend of adapting classic binarization algorithms with neural networks,
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+ such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work
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+ as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
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  ## Intended uses & limitations
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+ TBC
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  ## Training and evaluation data
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+ TBC
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  ## Training procedure
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  ### Training hyperparameters
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+ TBC
 
 
 
 
 
 
 
 
 
 
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  ### Training results
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+ | training loss | epoch | step | validation loss | DRD | F-measure | pseudo F-measure | PSNR |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:----------------:|:-------:|
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  | 0.6983 | 0.26 | 10 | 0.7079 | 199.5096 | 0.5945 | 0.5801 | 3.4552 |
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  | 0.6657 | 0.52 | 20 | 0.6755 | 149.2346 | 0.7006 | 0.6165 | 4.6752 |
 
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  | 0.0495 | 15.23 | 590 | 0.0745 | 5.9136 | 0.9841 | 0.9740 | 16.0458 |
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  | 0.0413 | 15.48 | 600 | 0.0743 | 5.9548 | 0.9840 | 0.9740 | 16.0119 |
114
 
 
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  ### Framework versions
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+ - transformers 4.31.0
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+ - torch 2.0.0
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+ - datasets 2.13.1
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+ - tokenizers 0.13.3
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