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configs/det/README.md

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+# DBNet
+
+<!--- Guideline: use url linked to abstract in ArXiv instead of PDF for fast loading.  -->
+
+> [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947)
+
+## Introduction
+
+DBNet is a segmentation-based scene text detection method. Segmentation-based methods are gaining popularity for scene
+text detection purposes as they can more accurately describe scene text of various shapes, such as curved text.  
+The drawback of current segmentation-based SOTA methods is the post-processing of binarization (conversion of
+probability maps into text bounding boxes) which often requires a manually set threshold (reduces prediction accuracy)
+and complex algorithms for grouping pixels (resulting in a considerable time cost during inference).  
+To eliminate the problem described above, DBNet integrates an adaptive threshold called Differentiable Binarization(DB)
+into the architecture. DB simplifies post-processing and enhances the performance of text detection.Moreover, it can be
+removed in the inference stage without sacrificing performance.[[1](#references)]
+
+![dbnet_architecture](https://user-images.githubusercontent.com/16683750/225589619-d50c506c-e903-4f59-a316-8b62586c73a9.png)
+<p style="text-align: center;"><em>Figure 1. Overall DBNet architecture</em></p>
+
+The overall architecture of DBNet is presented in _Figure 1._ It consists of multiple stages:
+
+1. Feature extraction from a backbone at different scales. ResNet-50 is used as a backbone, and features are extracted
+   from stages 2, 3, 4, and 5.
+2. The extracted features are upscaled and summed up with the previous stage features in a cascade fashion.
+3. The resulting features are upscaled once again to match the size of the largest feature map (from the stage 2) and
+   concatenated along the channel axis.
+4. Then, the final feature map (shown in dark blue) is used to predict both the probability and threshold maps by
+   applying 3×3 convolutional operator and two de-convolutional operators with stride 2.
+5. The probability and threshold maps are merged into one approximate binary map by the Differentiable binarization
+   module. The approximate binary map is used to generate text bounding boxes.
+
+## Results
+
+### ICDAR2015
+
+| **Model** | **Backbone** | **Pretrained** | **Recall** | **Precision** | **F-score** |
+|-----------|--------------|----------------|------------|---------------|-------------|
+| DBNet     | ResNet-50    | ImageNet       | 81.97%     | 86.05%        | 83.96%      |
+
+## Quick Start
+
+### Preparation
+
+#### Installation
+
+Please refer to the [installation instruction](https://github.com/mindspore-lab/mindocr#installation) in MindOCR.
+
+### Dataset preparation
+
+First, the dataset labels need to be converted. To do so,
+download [ICDAR2015](https://rrc.cvc.uab.es/?ch=4&com=downloads), and extract images and labels in a preferred folder.
+Then, use the following command to generate _training_ labels:
+
+```shell
+python tools/dataset_converters/convert.py --dataset_name=ic15 --task=det --image_dir=IMAGES_DIR --label_dir=LABELS_DIR --output_path=OUTPUT_PATH
+```
+
+Repeat this step to generate the _test_ labels.
+
+After the label files are generated, update `configs/det/db_r50_icdar15.yaml` configuration file with data paths,
+specifically the following parts:
+
+```yaml
+...
+train:
+  ckpt_save_dir: './tmp_det'
+  dataset_sink_mode: True
+  dataset:
+    type: DetDataset
+    dataset_root: /data/ocr_datasets                                  <------ HERE
+    data_dir: ic15/text_localization/train                            <------ HERE
+    label_file: ic15/text_localization/train/train_icdar15_label.txt  <------ HERE
+...
+eval:
+  dataset_sink_mode: False
+  dataset:
+    type: DetDataset
+    dataset_root: /data/ocr_datasets                                  <------ HERE
+    data_dir: ic15/text_localization/test                             <------ HERE
+    label_file: ic15/text_localization/test/test_icdar2015_label.txt  <------ HERE
+...
+```
+
+### Config explanation
+
+_DBNet_ consists of 3 parts: `backbone`, `neck`, and `head`. Specifically:
+
+```yaml
+model:
+  type: det
+  transform: null
+  backbone:
+    name: det_resnet50  # Only ResNet50 is supported at the moment
+    pretrained: True    # Whether to use weights pretrained on ImageNet
+  neck:
+    name: DBFPN         # FPN part of the DBNet
+    out_channels: 256
+    bias: False
+    use_asf: False      # Adaptive Scale Fusion module from DBNet++ (use it for DBNet++ only)
+  head:
+    name: DBHead
+    k: 50               # amplifying factor for Differentiable Binarization
+    bias: False
+    adaptive: True      # True for training, False for inference
+```
+
+[comment]: <> (The only difference between _DBNet_ and _DBNet++_ is in the _Adaptive Scale Fusion_ module, which is controlled by the `use_asf` parameter in the `neck` module.)
+
+### Training
+
+After preparing a dataset and setting the configuration, training can be started as follows:
+
+```shell
+python tools/train.py -c=configs/det/db_r50_icdar15.yaml
+```
+
+### Evaluation
+
+To evaluate the accuracy of the trained model, you can use `eval.py`. Please **add** an additional configuration
+parameter **ckpt_load_path** in `eval` section and set it to the path of the model checkpoint and then run:
+
+```shell
+python tools/eval.py -c=configs/det/db_r50_icdar15.yaml
+```
+
+## References
+
+<!--- Guideline: Citation format GB/T 7714 is suggested. -->
+
+[1] Minghui Liao, Zhaoyi Wan, Cong Yao, Kai Chen, Xiang Bai. Real-time Scene Text Detection with Differentiable Binarization. arXiv:1911.08947, 2019