AdvancedEAST-PyTorch is mainly inherited from AdvancedEAST, also we made some changes for better usage in PyTorch. If this project is helpful to you, welcome to star.
- writen in PyTorch, easy to read and run
- change the dataset into LMDB format, reduce I/O overhead
- added precision/recall/F1_score output which is helpful when training the model
- just run
train.pyto automatically start training
- config file:
cfg.py, control parameters - pre-process data:
preprocess.py, resize image - generate LMDB dataset:
imgs2LMDB.py - [optional] label data:
label.py, produce label info - define network:
model_VGG.py - define loss function:
losses.py - execute training:
train.py - read LMDB dataset:
dataset.py - predict:
predict.pyandnms.py - evaluate the model:
utils.py
- AdvancedEast
- python 3.6.5
- PyTorch-gpu 1.4.0
- lmdb 0.98
- numpy 1.19.0
- tqdm 4.48.0
- natsort 7.0.1
- openCV 4.2.0
- shapely 1.7.0
- [optional] torchsummary
-
tianchi ICPR dataset download 链接: https://pan.baidu.com/s/1NSyc-cHKV3IwDo6qojIrKA 密码: ye9y
-
prepare training data: make data root dir(train_1000), copy images to root dir, and copy txts to root dir, data format details could refer to ICPR MTWI 2018 挑战赛二：网络图像的文本检测
-
modify config params in
cfg.py, see default values -
[optional]
python preprocess.py, resize image to 256X256, 384X384, 512X512, 640X640, 736X736, and train one by one could speed up training process(依次训练可以加速模型收敛) -
[optional]
python imgs2LMDB.py, generate LMDB sataset -
python train.py, train entrance -
python predict.py -p demo/001.png, to predict -
pretrain model download(use for further training or test) 链接: 链接: https://pan.baidu.com/s/1q473YIt2b18RqpOT8rdY6g 提取码: nkit
The codes are released under the MIT License.