license_plate_recognition_with_yolov7's Introduction

Automatic number-plate recognition_with_yolov7

This project aims to conduct automatic number-plate recognition by YOLOv7 with OCR.

Setup the environment

conda create -n ANPR python=3.8
conda activate ANPR
pip install -r requirements.txt

Install pytorch from web

Download car plate dataset

Downloaded the dataset to the root of this project
name the folder "car-plate-detection".

Train the YOLOv7 model

Run the following command to preprocess the dataset and start transfer learning yolov7 to our application.

source setup.sh

Run the detection and OCR

You can inference this project by following command. If you try to add your own testing image, please put it into folder "license".

source detect_ocr.sh

cd yolov7
python detect.py --weights ./runs/train/yolov7-license/weights/best.pt --conf 0.25 --img-size 640 --source ../license --save-txt
cd ..
python OCR.py

Result

The resource used in this project as following:

YOLOv7

Paper : https://arxiv.org/abs/2207.02696
Code : https://github.com/WongKinYiu/yolov7

Datasets

Car License Plate Detection https://www.kaggle.com/datasets/andrewmvd/car-plate-detection

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license_plate_recognition_with_yolov7's Issues

setup error

Hello @shoghilin
When running the training command in the last line of the setup.sh script, I received the following error:

train: New cache created: Dataset/train/labels.cache
Traceback (most recent call last):
  File "train.py", line 616, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 245, in train
    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
  File "/home/ubuntu/Desktop/github/6.license_plate_recognition_with_yolov7/yolov7/utils/datasets.py", line 69, in create_dataloader
    dataset = LoadImagesAndLabels(path, imgsz, batch_size,
  File "/home/ubuntu/Desktop/github/6.license_plate_recognition_with_yolov7/yolov7/utils/datasets.py", line 408, in __init__
    labels, shapes, self.segments = zip(*cache.values())
ValueError: not enough values to unpack (expected 3, got 0)

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