Comments (8)
hello everyone i tried to use photos of the LP only without the vehicle and the model doesn't extract the text of the LP , so i notes that if it was a photo of a vehicle with the LP it will be detected otherwise it's not
Yeah! In the paper, the model will first detect the vehicle ,than detect the LP, so maybe if there is no vehicle in you photo , this model won't work well.
from alpr-unconstrained.
Actually , it does not need a vehicle just use the detect ocr script but the trained model that they shared is not the best , if you train your own it will detect the license plate or the ocr , if you have any questions feel free to ask , i will try to help you
from alpr-unconstrained.
Actually , it does not need a vehicle just use the detect ocr script but the trained model that they shared is not the best , if you train your own it will detect the license plate or the ocr , if you have any questions feel free to ask , i will try to help you
thank you very much , i'm actually working on the LP of Saudi Arabia and i trained my model for about 70K iteration on google colab because my computer is old so i used there GPU but after 70K iteration i got over usage of GPU in google colab . however when i run the model i only got 1 LP of saudi arabia is correct the rest of them either i got some of the letters or i got some of the numbers and this is my output , the only correct Saudi LP is the 4.1.jpg
Scanning /tmp/output/03016_0car_lp.png LP: MPE3389 Scanning /tmp/output/03025_0car_lp.png LP: INS6012 Scanning /tmp/output/03033_0car_lp.png LP: SEZ229 Scanning /tmp/output/03058_0car_lp.png LP: 1 Scanning /tmp/output/03058_1car_lp.png LP: C2LJBH Scanning /tmp/output/03066_2car_lp.png LP: HHP8586 Scanning /tmp/output/03071_0car_lp.png LP: 6GQR959 Scanning /tmp/output/1.13_0car_lp.png LP: J7L Scanning /tmp/output/2.19_0car_lp.png LP: RDJ Scanning /tmp/output/4.1_0car_lp.png LP: 5622ZVB Scanning /tmp/output/4.2_0car_lp.png LP: 19143A Scanning /tmp/output/46_0car_lp.png LP: MH20CS9817
from alpr-unconstrained.
Hi there,
I could detect the plate using different model, however, I used the your ocr and found good results, still, there is a problem, the detected plate need a deblure so the chars and numbers become more clearer and accurate
I really hope that you could help me in finding a great deblure model that could also work in a realtime manner
from alpr-unconstrained.
Hmm, this can mean that
Actually , it does not need a vehicle just use the detect ocr script but the trained model that they shared is not the best , if you train your own it will detect the license plate or the ocr , if you have any questions feel free to ask , i will try to help you
thank you very much , i'm actually working on the LP of Saudi Arabia and i trained my model for about 70K iteration on google colab because my computer is old so i used there GPU but after 70K iteration i got over usage of GPU in google colab . however when i run the model i only got 1 LP of saudi arabia is correct the rest of them either i got some of the letters or i got some of the numbers and this is my output , the only correct Saudi LP is the 4.1.jpg
Scanning /tmp/output/03016_0car_lp.png LP: MPE3389 Scanning /tmp/output/03025_0car_lp.png LP: INS6012 Scanning /tmp/output/03033_0car_lp.png LP: SEZ229 Scanning /tmp/output/03058_0car_lp.png LP: 1 Scanning /tmp/output/03058_1car_lp.png LP: C2LJBH Scanning /tmp/output/03066_2car_lp.png LP: HHP8586 Scanning /tmp/output/03071_0car_lp.png LP: 6GQR959 Scanning /tmp/output/1.13_0car_lp.png LP: J7L Scanning /tmp/output/2.19_0car_lp.png LP: RDJ Scanning /tmp/output/4.1_0car_lp.png LP: 5622ZVB Scanning /tmp/output/4.2_0car_lp.png LP: 19143A Scanning /tmp/output/46_0car_lp.png LP: MH20CS9817
Hmm i havent used google colab , but i you are detecting only couple of letters maybe that means that the training did not go well , try again with a lower batch size and iterations in the config file , and one more thing , are you training YOLOv3 model or YOLOv2? because the ocr script cant work with YOLOv3 it gives false results and incomplete , if you are using YOLOv3 i have scipt that can help you .
PS how many images are you training on ?
from alpr-unconstrained.
Hmm, this can mean that
Actually , it does not need a vehicle just use the detect ocr script but the trained model that they shared is not the best , if you train your own it will detect the license plate or the ocr , if you have any questions feel free to ask , i will try to help you
thank you very much , i'm actually working on the LP of Saudi Arabia and i trained my model for about 70K iteration on google colab because my computer is old so i used there GPU but after 70K iteration i got over usage of GPU in google colab . however when i run the model i only got 1 LP of saudi arabia is correct the rest of them either i got some of the letters or i got some of the numbers and this is my output , the only correct Saudi LP is the 4.1.jpg
Scanning /tmp/output/03016_0car_lp.png LP: MPE3389 Scanning /tmp/output/03025_0car_lp.png LP: INS6012 Scanning /tmp/output/03033_0car_lp.png LP: SEZ229 Scanning /tmp/output/03058_0car_lp.png LP: 1 Scanning /tmp/output/03058_1car_lp.png LP: C2LJBH Scanning /tmp/output/03066_2car_lp.png LP: HHP8586 Scanning /tmp/output/03071_0car_lp.png LP: 6GQR959 Scanning /tmp/output/1.13_0car_lp.png LP: J7L Scanning /tmp/output/2.19_0car_lp.png LP: RDJ Scanning /tmp/output/4.1_0car_lp.png LP: 5622ZVB Scanning /tmp/output/4.2_0car_lp.png LP: 19143A Scanning /tmp/output/46_0car_lp.png LP: MH20CS9817
Hmm i havent used google colab , but i you are detecting only couple of letters maybe that means that the training did not go well , try again with a lower batch size and iterations in the config file , and one more thing , are you training YOLOv3 model or YOLOv2? because the ocr script cant work with YOLOv3 it gives false results and incomplete , if you are using YOLOv3 i have scipt that can help you .
PS how many images are you training on ?
i used google colab because it better and easy to use with GPU . i used YOLOv2 , i triad with YOLOv3 but i got nothing . the Saudi LP is two layer the first in Arabic and the second in English but i only need the one in English you may google it so you can understand it better .and for the training i used 98 images with annotation Xml file , and for the annotation i used website called Nanonets.com to label and to download the annotation
from alpr-unconstrained.
maybe if i can used the available model by the developer to detect the whole second layer only i may have a better results .but the problem with Saudi LP is that it has many shapes the long rectangle LP and the short rectangle and some of the long rectangle LP has a word " KSA " written in the middle of the LP
from alpr-unconstrained.
Actually , it does not need a vehicle just use the detect ocr script but the trained model that they shared is not the best , if you train your own it will detect the license plate or the ocr , if you have any questions feel free to ask , i will try to help you
Have you trained your own?
from alpr-unconstrained.
Related Issues (20)
- The OCR operation only
- Does anyone ever upgraded the build environment? HOT 10
- Unable to load wpod model HOT 2
- using python2.7:vehicle-detection.py, line 28, vehicle_net = dn.load_net(vehicle_netcfg, vehicle_weights, 0) ctypes.ArgumentError: argument 1: wrong type # HOT 3
- Does someone ever tried to convert this to TensorFlow Lite to make it work on Android ? HOT 1
- about"bash get-networks.sh" HOT 1
- not detecting any cars from the sample images as well can it be because of python 3.7 ?? HOT 1
- affinex and affiney in loss.py
- training loss
- Explanation for detect_lp() two input params which are hardcoded
- 403 Forbidden for 'bash get-networks.sh' HOT 5
- Loss error HOT 1
- 'bash get-networks.sh' not working. HOT 1
- Running on google colab not detecting any car on test directory provided after fixing issue for running using python 3 HOT 4
- Website unreachable HOT 1
- List index out of range HOT 1
- License Plate Recognition in EVA multimedia database system
- Prediction Score
- Performance measurement
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from alpr-unconstrained.