Comments (13)
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Line 140 in 1fb74ea
you can add:
import sys, traceback
traceback.print_exc(file=sys.stdout)
after 'except:'
to see what is going on
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Thank you so much for your reply,it seems that in ocr_test_utils.py
this fuction needs four outputs but just unpack three,when i add one output
det_text, conf, dec_s, _=print_seq_ext()
the problem solves!
thanks you ~
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besides, i have one question to ask for your help~
can i just use the ocr model to train a seperate word recognition model(just to achieve recognition task).if i can do like this,how large dataset should i have?(i just want to test on icdar2015 word recognition dataset )
thank you ! @MichalBusta
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Could you please give me some advice?Thanks a lot~ @MichalBusta
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hello,when i use the dataset from IC15、IC17MLT&IC19MLT(only use the latin words,about 70000word images),and only run the train_ocr.py,but the accuracy on ic15 test word dataset can just achieve 56.3% accuracy ,the batchsize i used equals to 4,could you please give me some advice why the accuracy is so low,and what should i do to improve it ?
Thanks a lot~
@MichalBusta
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No easy answer sorry :)
- so low accuracy compared to who ( Baidu IDL, Alibaba PAI, Baidu VIS )?
what can help: - data, data, data ... (google PhotoOCR ~= 3mil manually annotated images, you should expect log scale dependency)
- language modeling
- more complicated model(s) (this project focus on real-time text detection), so you can go to seq-to-seq models ... good starer paper: http://openaccess.thecvf.com/content_cvpr_2016/html/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.html - quite old, but still good
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ok,i will try.thanks so much for your reply~
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@MichalBusta I also have some questions regarding your text recognition model.
1/ For latin languages, did you train your text recognition models with single word images only (i.e. no text lines)?
2/ How many images did you train your text recognition model with?
3/ Your text recognition model seems to use a ResNet like structure. Since this project focus on real-time, have you tried to train your text recognition model with a MobileNet backbone?
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Thanks @MichalBusta ! One more thing, for the 500k images, it consists of an equal share between Arabic, Bangla, Chinese, Japanese, Korean and Latin (i.e. ~80k images for each script)?
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Related Issues (20)
- Need English Result instead of Chinese HOT 1
- RuntimeError: Cannot compile nms: /home/miteng/E2E-MLT-master/nms
- RuntimeError: Cannot compile nms: HOT 3
- How to evaluate E2E-MLT? HOT 1
- Error While running demo script HOT 7
- The problem of "-ocr_feed_list" config HOT 4
- Missing license
- the problem of seg_loss HOT 1
- how to generate synth data for bangla and hindi languages?
- the problem about input_size HOT 1
- the problem about process_boxes HOT 4
- What is the structure of the dataset? HOT 1
- How did you happen use the data from SynthText HOT 6
- train_ocr.py training details
- Error Reloading the model from a checkpoint model HOT 1
- can i convert the model by torchscript?
- Data Link not working HOT 1
- 3 times call to function net.forward_ocr in method process_boxes HOT 2
- All loss values become nan HOT 2
- Affine grid parameters to crop predicted Bounding Boxes without transformation
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