simplify23 / cdistnet Goto Github PK

I trained the model with billboards but when I inference it, it doesn't work well with sequences of numbers or phone numbers. Can you help me? Thanks very much.

I tried your network and got a good result but I faced the problem of inference speed. could you please let me know I can increase the speed of recognition?

did you convert your model into onnx and tensorrt? could you please provide the code?

waiting for code release

I tried to train your model but I got accuracy is lower than other transformer models. could you please let me know how can I got higher accuracy ?

I have a relatively small dataset of a different format (license plates) and it often gets license plate format wrong.

I was wondering if there was a way to train the model on just a bunch of text string data without feeding any images at all in order to enforce the format.

Please let me know if it is possible to train the language/semantic model independently, by just feeding string text data of words, without corresponding images.

Hello,
I don't know what is result.csv and how to to get it?
Thank you

@simplify23 are you planning to release CDistNetv2 code?
waiting for light weight and faster module

hello thanks for your paper and released codes
I want to train your code for other language but I see in lmdbdataset that you use English char and limit the max length to 30 that is true?
I should change line 245 and 246?

`def len(self):
return self.length

def get(self,idx):
    with self.env.begin(write=False) as txn:
        image_key, label_key = f'image-{idx+1:09d}', f'label-{idx+1:09d}'
        label = str(txn.get(label_key.encode()), 'utf-8')  # label
        label = re.sub('[^0-9a-zA-Z]+', '', label)
        label = label[:30]`

Could you please release the code to generate the attention maps as published in the paper

Here i came to know that we are taking predictions on lmdb dataset. So is there any chance to get results out of images ?

I don't know how dict_36.txt can train with other characters like upper case.

Are you willing to specify an open-source license such as a MIT License?
The github has no license specified.

How to set the parameters of input_char，such as predict a new image

Hello, while examining the code,

I noticed that most of the nn.Linear() operations are replaced with nn.Conv2d(kernel_size=(1,1)) operations

when comparing nn.Transformer and the implementation of the code.

Is there a benefit for such replacement?

When trying to run test.py I get the following error:

(CDistNet) C:\<path>\CDistNet>python test.py --i_path ..\examples\300_0.jpg 
configs/CDistNet_config.py
<class 'str'>
Traceback (most recent call last):
  File "test.py", line 175, in <module>
    main()
  File "test.py", line 168, in main
    test_one(cfg, args)
  File "test.py", line 126, in test_one
    en = get_parameter_number(model.transformer.encoder)
  File "C:\<path>\miniconda3\envs\CDistNet\lib\site-packages\torch\nn\modules\module.py", line 1178, in __getattr__ 
    type(self).__name__, name))
AttributeError: 'CDistNet' object has no attribute 'transformer'