deep-text-recognition-benchmark 학습

환경설정

1. Conda 환경 생성

[root@server ~]$ conda create -n deep_text_recognition python=3.7
[root@server ~]$ conda activate deep_text_recognition
(deep_text_recognition) [root@server ~]$ conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=10.2 -c pytorch

2. CRAFT-pytorch 소스 다운로드

(deep_text_recognition) [root@server ~]$ https://github.com/clovaai/deep-text-recognition-benchmark.git
(deep_text_recognition) [root@server ~]$ cd deep-text-recognition-benchmark

 

3. PIP 패키지 설치

(craft_model) [root@server deep-text-recognition-benchmark] pip3 install lmdb pillow nltk natsort

 

데이터 생성

1. 원시 데이터 준비

train, valid 훈련 데이터셋을 다음과 같이 준비한다.

(craft_model) [root@server deep-text-recognition-benchmark] mkdir dataset
(craft_model) [root@server deep-text-recognition-benchmark] tree -n 2 dataset
dataset/
├── gt_train.txt
├── gt_valid.txt
├── train
│   ├── train-sample1.jpg
│   └── train-sample2.jpg
└── valid
    └── valid-sample1.jpg
 
2 directories, 5 files

 

텍스트 파일 내용은 탭 (\t) 기준으로 마지막에 개행(\n) 문자를 포함하여 생성하여 줍니다.
예) <이미지 경로>\t<문자>\n

(craft_model) [root@server deep-text-recognition-benchmark] cat dataset/gt_train.txt
train/train-sample1.jpg 가방
train/train-sample2.jpg 나비
(craft_model) [root@server deep-text-recognition-benchmark] cat dataset/gt_valid.txt
valid/valid-sample1.jpg 가방
valid/valid-sample2.jpg 나비

2. lmdb 파일 생성

train 데이터셋 생성

(craft_model) [root@server deep-text-recognition-benchmark] python create_lmdb_dataset.py \
--inputPath dataset/ \
--gtFile dataset/gt_char_train.txt \
--outputPath data_lmdb/train

 

valid 데이터셋 생성

(craft_model) [root@server deep-text-recognition-benchmark]$ python create_lmdb_dataset.py \
--inputPath dataset/ \
--gtFile dataset/gt_char_valid.txt \
--outputPath data_lmdb/valid

 

훈련

--character 반드시 학습할 문자로 설정 해줘야 합니다.
--select_data  '/'로 수정합니다.
--batch-ratio를 1로 수정합니다. (커스텀 데이터셋)

(craft_model) [root@server deep-text-recognition-benchmark]$ python train.py \
--train_data data_lmdb/train \
--valid_data data_lmdb/valid \
--Transformation TPS \
--FeatureExtraction ResNet \
--SequenceModeling BiLSTM \
--Prediction CTC \
--data_filtering_off \
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--select_data "/" \
--batch_ratio 1 \
--batch_size 10 \
--workers 0 \
--imgH 110 \
--imgW 110

 

다음과 같이 출력이 되며 학습이 진행된다.

...
[1/300000] Train loss: 170.54124, Valid loss: 169.87041, Elapsed_time: 1.47007
Current_accuracy : 0.000, Current_norm_ED  : 0.00
Best_accuracy    : 0.000, Best_norm_ED     : 0.00
--------------------------------------------------------------------------------
Ground Truth              | Prediction                | Confidence Score & T/F
--------------------------------------------------------------------------------
가방                      | 껏춘얻을든주을해든주약권              | 0.0000    False
나비                      | 맡춘해얻을마을교마교짧교짧주을주해    | 0.0000 False
--------------------------------------------------------------------------------
...
<출력 생략>