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@sirius-ai
Hello, Thanks for this wonderful repository
In my case, KOR licenseplate has 7digits or 8digits.
Type A , and Type B
Type A is like 12A1234
Type B is like 123B1234
the formats are 2-3 digits + One Letter + 4 digits
However, for some case it would return bad precision
Case1.
target : 36루5854 -> infer: 36루구5854
Case2.
target:97로6695 -> infer: 97로669두
Both cases have wrong format. this cannot be licenseplate format.
By this case "my model" has not learn the format
as I mentioned earlier, format has only one/two Letters between digits. no letters after digits.
I looked into model and found global context.
Q1. I found code for global context in lprnet model It has already been applied. So just training this model would get advantages of global context (such as formatting). Am I wrong?
Should I need some postprocessing functions for formatting?
Q2. Overfitting with this model , Ideas to deal with train data.
I have trained this model with only real car image datas 10k . Got about 90-95% percent Greed_decode_Eval Accuracy
But, when I apply this model to general cases, significantly drops to 80% accuracy
( even with good image : clean and nice view of licenseplate)
I think maybe the problem is overfitting , Since it has bad accuracy when tested with other license plate number images
Generated Fake License Plate Image (10k) + Real Images(10k) + Augmented Real Images(30k) training would help for this case?
I really need help. Thanks
The text was updated successfully, but these errors were encountered:
@sirius-ai
Hello, Thanks for this wonderful repository
In my case, KOR licenseplate has 7digits or 8digits.
Type A , and Type B
Type A is like 12A1234
Type B is like 123B1234
the formats are 2-3 digits + One Letter + 4 digits
However, for some case it would return bad precision
Case1.
target : 36루5854 -> infer: 36루구5854
Case2.
target:97로6695 -> infer: 97로669두
Both cases have wrong format. this cannot be licenseplate format.
By this case "my model" has not learn the format
as I mentioned earlier, format has only one/two Letters between digits. no letters after digits.
I looked into model and found global context.
Q1. I found code for global context in lprnet model It has already been applied. So just training this model would get advantages of global context (such as formatting). Am I wrong?
Should I need some postprocessing functions for formatting?
Q2. Overfitting with this model , Ideas to deal with train data.
I have trained this model with only real car image datas 10k . Got about 90-95% percent Greed_decode_Eval Accuracy
But, when I apply this model to general cases, significantly drops to 80% accuracy
( even with good image : clean and nice view of licenseplate)
I think maybe the problem is overfitting , Since it has bad accuracy when tested with other license plate number images
Generated Fake License Plate Image (10k) + Real Images(10k) + Augmented Real Images(30k) training would help for this case?
I really need help. Thanks
The text was updated successfully, but these errors were encountered: