From 973a9f41cc46c6874c9036dda14313dd73641ca4 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 9 Sep 2021 10:50:24 +0800 Subject: [PATCH] fix doc --- doc/doc_ch/detection.md | 70 ++++++++++++++++++++++---------------- doc/doc_en/detection_en.md | 46 ++++++++++++++----------- 2 files changed, 68 insertions(+), 48 deletions(-) diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md index 66295b25..fbc0b9e7 100644 --- a/doc/doc_ch/detection.md +++ b/doc/doc_ch/detection.md @@ -1,22 +1,25 @@ -# 目录 -- [1. 文字检测](#1-----) - * [1.1 数据准备](#11-----) - * [1.2 下载预训练模型](#12--------) - * [1.3 启动训练](#13-----) - * [1.4 断点训练](#14-----) - * [1.5 更换Backbone 训练](#15---backbone---) - * [1.6 指标评估](#16-----) - * [1.7 测试检测效果](#17-------) - * [1.8 转inference模型测试](#18--inference----) -- [2. FAQ](#2-faq) - - - -# 1. 文字检测 +# 文字检测 本节以icdar2015数据集为例，介绍PaddleOCR中检测模型训练、评估、测试的使用方式。 +- [1. 准备数据和模型](#1--------) + * [1.1 数据准备](#11-----) + * [1.2 下载预训练模型](#12--------) +- [2. 开始训练](#2-----) + * [2.1 启动训练](#21-----) + * [2.2 断点训练](#22-----) + * [2.3 更换Backbone 训练](#23---backbone---) +- [3. 模型评估与预测](#3--------) + * [3.1 指标评估](#31-----) + * [3.2 测试检测效果](#32-------) +- [4. 模型导出与预测](#4--------) + * [4.1 转inference模型测试](#41--inference----) +- [5. FAQ](#5-faq) + + +# 1. 准备数据和模型 + ## 1.1 数据准备 @@ -83,8 +86,11 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams ``` - -## 1.3 启动训练 + +# 2. 开始训练 + + +## 2.1 启动训练 *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false* @@ -106,8 +112,8 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001 ``` - -## 1.4 断点训练 + +## 2.2 断点训练 如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径： ```shell @@ -116,8 +122,8 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you **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。 - -## 1.5 更换Backbone 训练 + +## 2.3 更换Backbone 训练 PaddleOCR将网络划分为四部分，分别在[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。 @@ -164,8 +170,11 @@ args1: args1 **注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。 - -## 1.6 指标评估 + +# 3. 模型评估与预测 + + +## 3.1 指标评估 PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean（F-Score）。 @@ -177,8 +186,8 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat * 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置 - -## 1.7 测试检测效果 + +## 3.2 测试检测效果 测试单张图像的检测效果 ```shell @@ -195,8 +204,11 @@ python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./ python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" ``` - -## 1.8 转inference模型测试 + +# 4. 模型导出与预测 + + +## 4.1 转inference模型测试 inference 模型（`paddle.jit.save`保存的模型） 一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。 @@ -218,8 +230,8 @@ python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./outpu python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True ``` - -# 2. FAQ + +# 5. FAQ Q1: 训练模型转inference 模型之后预测效果不一致？ **A**：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。以det_mv3_db.yml配置文件训练的模型为例，训练模型、inference模型预测结果不一致问题解决方式如下： diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md index d3f6f3da..bdfde672 100644 --- a/doc/doc_en/detection_en.md +++ b/doc/doc_en/detection_en.md @@ -1,21 +1,22 @@ -# CONTENT +# TEXT DETECTION -- [Paste Your Document In Here](#paste-your-document-in-here) -- [1. TEXT DETECTION](#1-text-detection) +This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR. + +- [1. DATA AND WEIGHTS PREPARATIO](#1-data-and-weights-preparatio) * [1.1 DATA PREPARATION](#11-data-preparation) * [1.2 DOWNLOAD PRETRAINED MODEL](#12-download-pretrained-model) - * [1.3 START TRAINING](#13-start-training) - * [1.4 LOAD TRAINED MODEL AND CONTINUE TRAINING](#14-load-trained-model-and-continue-training) - * [1.5 TRAINING WITH NEW BACKBONE](#15-training-with-new-backbone) - * [1.6 EVALUATION](#16-evaluation) - * [1.7 TEST](#17-test) - * [1.8 INFERENCE MODEL PREDICTION](#18-inference-model-prediction) +- [2. TRAINING](#2-training) + * [2.1 START TRAINING](#21-start-training) + * [2.2 LOAD TRAINED MODEL AND CONTINUE TRAINING](#22-load-trained-model-and-continue-training) + * [2.3 TRAINING WITH NEW BACKBONE](#23-training-with-new-backbone) +- [3. EVALUATION AND TEST](#3-evaluation-and-test) + * [3.1 EVALUATION](#31-evaluation) + * [3.2 TEST](#32-test) +- [4. INFERENCE](#4-inference) + * [4.1 INFERENCE MODEL PREDICTION](#41-inference-model-prediction) - [2. FAQ](#2-faq) - -# 1. TEXT DETECTION - -This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR. +# 1 DATA AND WEIGHTS PREPARATIO ## 1.1 DATA PREPARATION @@ -75,7 +76,10 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg ``` -## 1.3 START TRAINING +# 2. TRAINING + +## 2.1 START TRAINING + *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.* ```shell python3 tools/train.py -c configs/det/det_mv3_db.yml \ @@ -98,7 +102,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs ``` -## 1.4 LOAD TRAINED MODEL AND CONTINUE TRAINING +## 2.2 LOAD TRAINED MODEL AND CONTINUE TRAINING If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded. For example: @@ -109,7 +113,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you **Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded. -## 1.5 TRAINING WITH NEW BACKBONE +## 2.3 TRAINING WITH NEW BACKBONE The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones-> necks->heads). @@ -159,7 +163,9 @@ After adding the four-part modules of the network, you only need to configure th **NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md). -## 1.6 EVALUATION +# 3. EVALUATION AND TEST + +## 3.1 EVALUATION PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean(F-Score). @@ -174,7 +180,7 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat * Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST and SAST model. -## 1.7 TEST +## 3.2 TEST Test the detection result on a single image: ```shell @@ -192,7 +198,9 @@ Test the detection result on all images in the folder: python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" ``` -## 1.8 INFERENCE MODEL PREDICTION +# 4. INFERENCE + +## 4.1 INFERENCE MODEL PREDICTION 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. -- GitLab