Updated README with DIBCO metrics

README.md

@@ -1,11 +1,13 @@
 ---
-license: other
+license: openrail
 tags:
-- document-image-binarizationimage-segmentation
+- document-image-binarization
+- image-segmentation
 - generated_from_trainer
 model-index:
 - name: binarization-segformer-b3
   results: []
+pipeline_tag: image-to-image
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,17 +15,19 @@ should probably proofread and complete it, then remove this comment. -->
 
 # binarization-segformer-b3
 
-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.
-It achieves the following results on the evaluation set:
-- Loss: 0.1017
-- Fmeasure: 0.9776
-- Pfmeasure: 0.9531
-- Psnr: 14.5040
-- Drd: 5.3749
+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 same ensemble of datasets as the [SauvolaNet work](https://arxiv.org/pdf/2105.05521.pdf). The ensemble is publicly available in the official [SauvolaNet repository](https://github.com/Leedeng/SauvolaNet#datasets).
+It achieves the following results on the evaluation set on DIBCO metrics:
+- loss: 0.1017
+- F-measure: 0.9776
+- probabilistic F-measure: 0.9531
+- PSNR: 14.5040
+- DRD: 5.3749
+
+For more information on DIBCO metrics, see the [paper](https://ieeexplore.ieee.org/document/8270159) in which they were introduced.
 
 ## Model description
 
-More information needed
+This model is part of on-going research on pure semantic segmentation models for document image binarization.
 
 ## Intended uses & limitations
 
@@ -51,7 +55,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Fmeasure | Pfmeasure | Psnr    | Drd      |
+| training loss | epoch | step | validation loss | F-measure | probabilistic 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  |
@@ -105,4 +109,4 @@ The following hyperparameters were used during training:
 - Transformers 4.27.4
 - Pytorch 2.0.0+cu118
 - Datasets 2.11.0
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3