Yolov5n License Plate

🚀 yolov5n-车牌检测模型

本项目基于YOLOv5实现车牌目标检测，具有高精度的检测效果，可应用于车牌识别等视觉相关场景。

🚀 快速开始

安装依赖

安装 yolov5：

pip install -U yolov5

加载模型并进行预测

import yolov5

# load model
model = yolov5.load('keremberke/yolov5n-license-plate')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

在自定义数据集上微调模型

yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-license-plate --epochs 10

更多模型

更多模型请参考：awesome-yolov5-models

📚 详细文档

模型信息

评估指标

模型在keremberke/license-plate-object-detection验证集上的评估指标如下：

模型可视化