pytorch、TensorFlow、深度学习实现基于EAST自然文本检测系统.zip

# EAST: An Efficient and Accurate Scene Text Detector ### Description: This version will be updated soon, please pay attention to this work. The motivation of this version is to build a easy-training model. This version can automatically update best_model by comparing current hmean and the former. At the same time, we can see evaluation info about every sample easily. + 1.train + 2.predict + 3.compress + 4.compute Hmean(if Hmean is higher than before, update best_weight.pkl) + 5.visualization(blue, green, red) + 6.multi-scale test (update soon) multi-scale vis. (vis with score, scales) ### Thanks The version is ported from [argman/EAST](https://github.com/argman/EAST), from Tensorflow to Pytorch ### Check On Website If you have no confidence of the result of our program, you could use submit.zip to submit on [website](http://rrc.cvc.uab.es/?ch=2&com=mymethods&task=1),then you can see result of every image. ### Performance + right -- green || wrong -- red || miss -- blue   + recall/precision/hmean for every test image  ### Introduction This is a pytorch re-implementation of [EAST: An Efficient and Accurate Scene Text Detector](https://arxiv.org/abs/1704.03155v2). The features are summarized blow: + Only **RBOX** part is implemented. + A fast Locality-Aware NMS in C++ provided by the paper's author.(g++/gcc version 6.0 + will be ok) + Evalution see [here](http://rrc.cvc.uab.es/?ch=4&com=evaluation&view=method_samples&task=1&m=29855&gtv=1) for the detailed results. + Differences from original paper + Use ResNet-50 rather than PVANET + Use dice loss (optimize IoU of segmentation) rather than balanced cross entropy + Use linear learning rate decay rather than staged learning rate decay Thanks for the author's ([@zxytim](https://github.com/zxytim)) help! Please cite his [paper](https://arxiv.org/abs/1704.03155v2) if you find this useful. ### Contents 1. [Installation](#installation) 2. [Download](#download) 3. [Prepare dataset/pretrain](#dataset) 4. [Test](#train) 5. [Train](#test) 6. [Examples](#examples) ### Installation 1. Any version of pytorch version > 0.4.0 should be ok. ### Download 1. Pretrained model is not provided temporarily. Web site is updating now, please continue to pay attention ### Prepare dataset/pretrain weight [1]. dataset(you need to prepare for dataset for train and test) suggestions: you could do a soft-link to root_to_this_program/dataset/train/img/*.jpg + -- train ./dataset/train/img/img_###.jpg ./dataset/train/gt/img_###.txt (you need to change name) + -- test ./data/test/img_###.jpg (img only) + -- gt.zip ./result/gt.zip(ICDAR15 gt.zip is avaliable on [website](http://rrc.cvc.uab.es/?ch=2&com=mymethods&task=1) ** Note: you can download dataset here + -- [ICDAR15](http://rrc.cvc.uab.es/?ch=4&com=downloads) + -- [ICDAR13](http://rrc.cvc.uab.es/?ch=2&com=downloads) [2]. pretrained + In config.py set resume True and set checkpoint path/to/weight/file + I will provide pretrianed weight soon [3]. check GPUs and CPUs you can use following to check aviliable gpu, this is for train ``` watch -n 0.1 nvidia-smi ``` then, you will see 2,3 is avaliable, modify config.py gpu_ids = [0,1], gpu = 2, and modify run.sh - CUDA_VISIBLE_DEVICES=2,3 ### Train If you want to train the model, you should provide the dataset path in config.py and run ``` sh run.py ``` ** Note: you should modify run.sh to specify your gpu id If you have more than one gpu, you can pass gpu ids to gpu_list(like gpu_list=0,1,2,3) in config.py ** Note: you should change the gt text file of icdar2015's filename to img_\*.txt instead of gt_img_\*.txt(or you can change the code in icdar.py), and some extra characters should be removed from the file. See the examples in training_samples/** ### Test By default, we set train-eval process into integer. If you want to use eval independently, you can do it by yourself. Any question can contact me. ### Examples Here are some test examples on icdar2015, enjoy the beautiful text boxes!