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[1_5_1] 물류 recognition 데이터의 유효성을 확인한다.

상태
In progress
담당
마감일
2022/11/25
요약
1. 랜덤하게 선택한 200장을 시각화해볼 때 문제가 없어야 하며, 2. 1% 모델로 10% 데이터, 10% 데이터로 100% 데이터가 문제 없이 추론가능해야 함.
선행 태스크 상태
In progress
관련 마일스톤
3 more properties
•
1% 의 물류 recognition 데이터로 학습시킨 모델로 10%, 100% 테스트 데이터에 대해 추론을 시키는 경우
1% from the scratch (Aihub 물류 train)
1% transfer learned (Aihub 물류 train)
10% from the scratch (Aihub 물류 train)
10% transfer learned (Aihub 물류 train)
IC2015 word acc
1% word acc
10% word acc
100% word acc
•
1% transfer learned
nohup tools/dist_train.sh \ configs/textrecog/sar/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained.py \ 2 > nohup.out &
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train
cp work_dirs/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained/epoch_100.pth \ pretrained/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained_stilted-grass-112.pth nohup tools/dist_test.sh \ configs/textrecog/sar/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained.py \ pretrained/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained_stilted-grass-112.pth \ 2 > nohup.out &
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eval
11/29 01:45:16 - mmengine - INFO - Epoch(val) [100][305/305] AihubTransit/recog/word_acc: 0.9467 AihubTransit/recog/word_acc_ignore_case: 0.9477 AihubTransit/recog/word_acc_ignore_case_symbol: 0.9499 IC15/recog/word_acc: 0.9470 IC15/recog/word_acc_ignore_case: 0.9490 IC15/recog/word_acc_ignore_case_symbol: 0.9533 AihubTransit/recog/char_recall: 0.9678 AihubTransit/recog/char_precision: 0.9658 IC15/recog/char_recall: 0.9704 IC15/recog/char_precision: 0.9657
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eval ret
python3 -m mmocr.ocr \ --det-config configs/textdet/dbnet/dbnet_resnet18_fpnc_2e_aihubtransit100of100.py \ --det-ckpt pretrained/dbnet_resnet18_fpnc_2e_aihubtransit100of100_hopeful-leaf-117.pth \ --recog-config configs/textrecog/sar/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained.py \ --recog-ckpt pretrained/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained_stilted-grass-112.pth \ data/det/aihub_transit/part_1of100/train/IMG_OCR_6_T_BL-F001_0020.png \ --img-out-dir work_dirs/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained \ --pred-out-file work_dirs/sar_resnet31_parallel-decoder_100e_aihubtransit1of100_pretrained/output.pkl \ --device cpu
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e2e