diff --git a/PPOCRLabel/README.md b/PPOCRLabel/README.md index 2d6e7f98bb2c2dc4d1c696628e45f4649bf84c1c..9e5b3245b0cfb56d300155a94f64d38edcdbb599 100644 --- a/PPOCRLabel/README.md +++ b/PPOCRLabel/README.md @@ -34,10 +34,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w pip3 install --upgrade pip # If you have cuda9 or cuda10 installed on your machine, please run the following command to install -python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple # If you only have cpu on your machine, please run the following command to install -python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple ``` For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation. diff --git a/PPOCRLabel/README_ch.md b/PPOCRLabel/README_ch.md index ecc2ab600eaf6bcfe71923f7fc6a9de82fa54ba7..7f9351dfe185be2417162f2c786f5eec0b58816a 100644 --- a/PPOCRLabel/README_ch.md +++ b/PPOCRLabel/README_ch.md @@ -37,11 +37,11 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P pip3 install --upgrade pip 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装 -python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple 如果您的机器是CPU，请运行以下命令安装 -python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple +python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple ``` 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md index 039aecf1ba3d6c1c717bafbecdb117416a1acc32..48de51ae726e662f48d465b8489a494448dafac1 100644 --- a/deploy/cpp_infer/readme_en.md +++ b/deploy/cpp_infer/readme_en.md @@ -1,4 +1,4 @@ -# Server-side C++ inference +# Server-side C++ Inference This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md). C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used. @@ -6,14 +6,14 @@ This section will introduce how to configure the C++ environment and complete it PaddleOCR model deployment. -## 1. Prepare the environment +## 1. Prepare the Environment ### Environment - Linux, docker is recommended. -### 1.1 Compile opencv +### 1.1 Compile OpenCV * First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows. @@ -73,7 +73,7 @@ opencv3/ |-- share ``` -### 1.2 Compile or download or the Paddle inference library +### 1.2 Compile or Download or the Paddle Inference Library * There are 2 ways to obtain the Paddle inference library, described in detail below. @@ -136,7 +136,7 @@ build/paddle_inference_install_dir/ Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library. -## 2. Compile and run the demo +## 2. Compile and Run the Demo ### 2.1 Export the inference model @@ -183,7 +183,7 @@ or the generated Paddle inference library path (`build/paddle_inference_install_ Execute the built executable file: ```shell ./build/ppocr [--param1] [--param2] [...] -``` +``` Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically, ##### 1. run det demo: diff --git a/doc/doc_ch/add_new_algorithm.md b/doc/doc_ch/add_new_algorithm.md index f66e26b4c13ae19460c44d80b85eb253c2accfde..79c29249dd7dd0b25ffa7625d11ed2378bfafec4 100644 --- a/doc/doc_ch/add_new_algorithm.md +++ b/doc/doc_ch/add_new_algorithm.md @@ -2,16 +2,18 @@ PaddleOCR将一个算法分解为以下几个部分，并对各部分进行模块化处理，方便快速组合出新的算法。 -* 数据加载和处理 -* 网络 -* 后处理 -* 损失函数 -* 指标评估 -* 优化器 +* [1. 数据加载和处理](#1) +* [2. 网络](#2) +* [3. 后处理](#3) +* [4. 损失函数](#4) +* [5. 指标评估](#5) +* [6. 优化器](#6) 下面将分别对每个部分进行介绍，并介绍如何在该部分里添加新算法所需模块。 -## 数据加载和处理 + + +## 1. 数据加载和处理 数据加载和处理由不同的模块(module)组成，其完成了图片的读取、数据增强和label的制作。这一部分在[ppocr/data](../../ppocr/data)下。 各个文件及文件夹作用说明如下: @@ -64,7 +66,9 @@ transforms: keep_keys: [ 'image', 'label' ] # dataloader will return list in this order ``` -## 网络 + + +## 2. 网络 网络部分完成了网络的组网操作，PaddleOCR将网络划分为四部分，这一部分在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones-> necks->heads)依次通过这四个部分。 @@ -123,7 +127,9 @@ Architecture: args1: args1 ``` -## 后处理 + + +## 3. 后处理 后处理实现解码网络输出获得文本框或者识别到的文字。这一部分在[ppocr/postprocess](../../ppocr/postprocess)下。 PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的后处理模块，对于没有内置的组件可通过如下步骤添加: @@ -171,7 +177,9 @@ PostProcess: args2: args2 ``` -## 损失函数 + + +## 4. 损失函数 损失函数用于计算网络输出和label之间的距离。这一部分在[ppocr/losses](../../ppocr/losses)下。 PaddleOCR内置了DB,EAST,SAST,CRNN和Attention等算法相关的损失函数模块，对于没有内置的模块可通过如下步骤添加: @@ -208,7 +216,9 @@ Loss: args2: args2 ``` -## 指标评估 + + +## 5. 指标评估 指标评估用于计算网络在当前batch上的性能。这一部分在[ppocr/metrics](../../ppocr/metrics)下。 PaddleOCR内置了检测，分类和识别等算法相关的指标评估模块，对于没有内置的模块可通过如下步骤添加: @@ -262,7 +272,9 @@ Metric: main_indicator: acc ``` -## 优化器 + + +## 6. 优化器 优化器用于训练网络。优化器内部还包含了网络正则化和学习率衰减模块。 这一部分在[ppocr/optimizer](../../ppocr/optimizer)下。 PaddleOCR内置了`Momentum`,`Adam` 和`RMSProp`等常用的优化器模块，`Linear`,`Cosine`,`Step`和`Piecewise`等常用的正则化模块与`L1Decay`和`L2Decay`等常用的学习率衰减模块。 diff --git a/doc/doc_ch/quickstart.md b/doc/doc_ch/quickstart.md index 8f57489059e7f8ac1fde11d9e5c382e2f7e85a18..1896d7a137f0768c6b2a8e0c02b18ff61fbfd03c 100644 --- a/doc/doc_ch/quickstart.md +++ b/doc/doc_ch/quickstart.md @@ -90,10 +90,10 @@ cd /path/to/ppocr_img ``` -如需使用2.0模型，请指定参数`--version 2.0`，paddleocr默认使用2.1模型。更多whl包使用可参考[whl包文档](./whl.md) - +如需使用2.0模型，请指定参数`--version PP-OCR`，paddleocr默认使用2.1模型(`--versioin PP-OCRv2`)。更多whl包使用可参考[whl包文档](./whl.md) + #### 2.1.2 多语言模型 Paddleocr目前支持80个语种，可以通过修改`--lang`参数进行切换，对于英文模型，指定`--lang=en`。 diff --git a/doc/doc_en/angle_class_en.md b/doc/doc_en/angle_class_en.md index dd7cc1e4b916b9cdb7f99600710bcb844e790f90..8861b54ce97d082b783cd4d7fdffbec560821174 100644 --- a/doc/doc_en/angle_class_en.md +++ b/doc/doc_en/angle_class_en.md @@ -1,13 +1,14 @@ -# TEXT ANGLE CLASSIFICATION +# Text Angle Classification -- [Method Introduction](#method-introduction) -- [Data Preparation](#data-preparation) -- [Training](#training) -- [Evaluation](#evaluation) -- [Prediction](#prediction) +- [1. Method Introduction](#method-introduction) +- [2. Data Preparation](#data-preparation) +- [3. Training](#training) +- [4. Evaluation](#evaluation) +- [5. Prediction](#prediction) -## Method Introduction + +## 1. Method Introduction The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system, The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support. @@ -16,7 +17,7 @@ Example of 0 and 180 degree data samples： ![](../imgs_results/angle_class_example.jpg) -## Data Preparation +## 2. Data Preparation Please organize the dataset as follows: @@ -72,7 +73,7 @@ containing all images (test) and a cls_gt_test.txt. The structure of the test se | ... ``` -## Training +## 3. Training Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file. PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. @@ -117,7 +118,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend **Note that the configuration file for prediction/evaluation must be consistent with the training.** -## Evaluation +## 4. Evaluation The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file. @@ -127,7 +128,7 @@ export CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy ``` -## Prediction +## 5. Prediction * Training engine prediction diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md index 4ac6758ff642a58e265e12a0be8308d1fb8251c0..aa78263e4b73a3ac35250e5483a394ab77450c90 100644 --- a/doc/doc_en/config_en.md +++ b/doc/doc_en/config_en.md @@ -1,4 +1,12 @@ -## Optional parameter list +# Configuration + +- [1. Optional Parameter List](#1-optional-parameter-list) +- [2. Intorduction to Global Parameters of Configuration File](#2-intorduction-to-global-parameters-of-configuration-file) +- [3. Multilingual Config File Generation](#3-multilingual-config-file-generation) + + + +## 1. Optional Parameter List The following list can be viewed through `--help` @@ -7,7 +15,9 @@ The following list can be viewed through `--help` | -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** | | -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false | -## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE + + +## 2. Intorduction to Global Parameters of Configuration File Take rec_chinese_lite_train_v2.0.yml as an example ### Global @@ -121,8 +131,9 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck | drop_last | Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size | True | \ | | num_workers | The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process | 8 | \ | + -## 3. MULTILINGUAL CONFIG FILE GENERATION +## 3. Multilingual Config File Generation PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。 @@ -187,21 +198,21 @@ Italian is made up of Latin letters, so after executing the command, you will ge ... character_type: it # language character_dict_path: {path/of/dict} # path of dict - + Train: dataset: name: SimpleDataSet data_dir: train_data/ # root directory of training data label_file_list: ["./train_data/train_list.txt"] # train label path ... - + Eval: dataset: name: SimpleDataSet data_dir: train_data/ # root directory of val data label_file_list: ["./train_data/val_list.txt"] # val label path ... - + ``` diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index e30355fb8e29031bd4ce040a86ad0f57d18ce398..907c889f4846b6892e0e7833db01326792f19cf7 100755 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -1,5 +1,5 @@ -# Reasoning based on Python prediction engine +# Inference based on Python Prediction Engine The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment. @@ -10,21 +10,21 @@ For more details, please refer to the document [Classification Framework](https: Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model. -- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT) - - [Convert detection model to inference model](#Convert_detection_model) - - [Convert recognition model to inference model](#Convert_recognition_model) - - [Convert angle classification model to inference model](#Convert_angle_class_model) +- [1. Convert Training Model to Inference Model](#CONVERT) + - [1.1 Convert Detection Model to Inference Model](#Convert_detection_model) + - [1.2 Convert Recognition Model to Inference Model](#Convert_recognition_model) + - [1.3 Convert Angle Classification Model to Inference Model](#Convert_angle_class_model) -- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE) - - [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION) - - [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION) - - [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION) - - [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION) +- [2. Text Detection Model Inference](#DETECTION_MODEL_INFERENCE) + - [2.1 Lightweight Chinese Detection Model Inference](#LIGHTWEIGHT_DETECTION) + - [2.2 DB Text Detection Model Inference](#DB_DETECTION) + - [2.3 East Text Detection Model Inference](#EAST_DETECTION) + - [2.4 Sast Text Detection Model Inference](#SAST_DETECTION) - [5. Multilingual model inference](#Multilingual model inference) -- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE) - - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION) +- [3. Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE) + - [3.1 Lightweight Chinese Text Recognition Model Reference](#LIGHTWEIGHT_RECOGNITION) - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION) - [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION) - [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS) @@ -38,9 +38,9 @@ Next, we first introduce how to convert a trained model into an inference model, - [2. OTHER MODELS](#OTHER_MODELS) -## CONVERT TRAINING MODEL TO INFERENCE MODEL +## 1. Convert Training Model to Inference Model -### Convert detection model to inference model +### 1.1 Convert Detection Model to Inference Model Download the lightweight Chinese detection model: ``` @@ -67,7 +67,7 @@ inference/det_db/ ``` -### Convert recognition model to inference model +### 1.2 Convert Recognition Model to Inference Model Download the lightweight Chinese recognition model: ``` @@ -95,7 +95,7 @@ inference/det_db/ ``` -### Convert angle classification model to inference model +### 1.3 Convert Angle Classification Model to Inference Model Download the angle classification model: ``` @@ -122,13 +122,13 @@ inference/det_db/ -## TEXT DETECTION MODEL INFERENCE +## 2. Text Detection Model Inference The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model. Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**. -### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE +### 2.1 Lightweight Chinese Detection Model Inference For lightweight Chinese detection model inference, you can execute the following commands: @@ -163,7 +163,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di ``` -### 2. DB TEXT DETECTION MODEL INFERENCE +### 2.2 DB Text Detection Model Inference First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert: @@ -184,7 +184,7 @@ The visualized text detection results are saved to the `./inference_results` fol **Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images. -### 3. EAST TEXT DETECTION MODEL INFERENCE +### 2.3 EAST TEXT DETECTION MODEL INFERENCE First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert: @@ -205,7 +205,7 @@ The visualized text detection results are saved to the `./inference_results` fol -### 4. SAST TEXT DETECTION MODEL INFERENCE +### 2.4 Sast Text Detection Model Inference #### (1). Quadrangle text detection model (ICDAR2015) First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert: @@ -243,13 +243,13 @@ The visualized text detection results are saved to the `./inference_results` fol **Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases. -## TEXT RECOGNITION MODEL INFERENCE +## 3. Text Recognition Model Inference The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details. -### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE +### 3.1 Lightweight Chinese Text Recognition Model Reference For lightweight Chinese recognition model inference, you can execute the following commands: diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md index c4fb5068197c8fb655c1e3ddf4aa6143e7d558e2..0055d8f7a89d0d218d001ea94fd4c620de5d037f 100644 --- a/doc/doc_en/quickstart_en.md +++ b/doc/doc_en/quickstart_en.md @@ -5,7 +5,7 @@ + [1. Install PaddleOCR Whl Package](#1-install-paddleocr-whl-package) * [2. Easy-to-Use](#2-easy-to-use) - + [2.1 Use by command line](#21-use-by-command-line) + + [2.1 Use by Command Line](#21-use-by-command-line) - [2.1.1 English and Chinese Model](#211-english-and-chinese-model) - [2.1.2 Multi-language Model](#212-multi-language-model) - [2.1.3 Layout Analysis](#213-layoutAnalysis) @@ -39,7 +39,7 @@ pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+ -### 2.1 Use by command line +### 2.1 Use by Command Line PaddleOCR provides a series of test images, click [here](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip) to download, and then switch to the corresponding directory in the terminal @@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag ['PAIN', 0.990372] ``` -If you need to use the 2.0 model, please specify the parameter `--version 2.0`, paddleocr uses the 2.1 model by default. More whl package usage can be found in [whl package](./whl_en.md) +If you need to use the 2.0 model, please specify the parameter `--version PP-OCR`, paddleocr uses the 2.1 model by default(`--versioin PP-OCRv2`). More whl package usage can be found in [whl package](./whl_en.md) #### 2.1.2 Multi-language Model diff --git a/ppstructure/table/README_ch.md b/ppstructure/table/README_ch.md index e580debaebd2425786e84bedb13301c2f0bb09d3..2e90ad33423da347b5a51444f2be53ed2eb67a7a 100644 --- a/ppstructure/table/README_ch.md +++ b/ppstructure/table/README_ch.md @@ -1,6 +1,16 @@ # 表格识别 +* [1. 表格识别 pipeline](#1) +* [2. 性能](#2) +* [3. 使用](#3) + + [3.1 快速开始](#31) + + [3.2 训练](#32) + + [3.3 评估](#33) + + [3.4 预测](#34) + + ## 1. 表格识别 pipeline + 表格识别主要包含三个模型 1. 单行文本检测-DB 2. 单行文本识别-CRNN @@ -17,6 +27,8 @@ 3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。 4. 单元格的识别结果和表格结构一起构造表格的html字符串。 + + ## 2. 性能 我们在 PubTabNet[1] 评估数据集上对算法进行了评估，性能如下 @@ -26,8 +38,9 @@ | EDD[2] | 88.3 | | Ours | 93.32 | + ## 3. 使用 - + ### 3.1 快速开始 ```python @@ -48,7 +61,7 @@ python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_ta 运行完成后，每张图片的excel表格会保存到output字段指定的目录下 note: 上述模型是在 PubLayNet 数据集上训练的表格识别模型，仅支持英文扫描场景，如需识别其他场景需要自己训练模型后替换 `det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。 - + ### 3.2 训练 在这一章节中，我们仅介绍表格结构模型的训练，[文字检测](../../doc/doc_ch/detection.md)和[文字识别](../../doc/doc_ch/recognition.md)的模型训练请参考对应的文档。 @@ -75,7 +88,7 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo **注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。 - + ### 3.3 评估 表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下: @@ -100,7 +113,7 @@ python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_di ```bash teds: 93.32 ``` - + ### 3.4 预测 ```python