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简体中文 | English
PaddleDetection团队提供了针对自动驾驶场景的基于PP-YOLOE的检测模型,用户可以下载模型进行使用,主要包含5个数据集(BDD100K-DET、BDD100K-MOT、UA-DETRAC、PPVehicle9cls、PPVehicle)。其中前3者为公开数据集,后两者为整合数据集。
pedestrian(1), rider(2), car(3), truck(4), bus(5), train(6), motorcycle(7), bicycle(8), traffic light(9), traffic sign(10)
。pedestrian(1), rider(2), car(3), truck(4), bus(5), train(6), motorcycle(7), bicycle(8)
,但数据集比BDD100K-DET更大更多。car(1), bus(2), van(3), others(4)
。pedestrian(1), rider(2), car(3), truck(4), bus(5), van(6), motorcycle(7), bicycle(8), others(9)
。car, truck, bus, van
和UA-DETRAC中的car, bus, van
都合并为1类vehicle(1)
后的数据集。模型 | 数据集 | 类别数 | mAPval 0.5:0.95 |
下载链接 | 配置文件 |
---|---|---|---|---|---|
PP-YOLOE-l | BDD100K-DET | 10 | 35.6 | 下载链接 | 配置文件 |
PP-YOLOE-l | BDD100K-MOT | 8 | 33.7 | 下载链接 | 配置文件 |
PP-YOLOE-l | UA-DETRAC | 4 | 51.4 | 下载链接 | 配置文件 |
PP-YOLOE-l | PPVehicle9cls | 9 | 40.0 | 下载链接 | 配置文件 |
PP-YOLOE-s | PPVehicle9cls | 9 | 35.3 | 下载链接 | 配置文件 |
PP-YOLOE-l | PPVehicle | 1 | 63.9 | 下载链接 | 配置文件 |
PP-YOLOE-s | PPVehicle | 1 | 61.3 | 下载链接 | 配置文件 |
注意:
TestDataset:
!ImageFolder
anno_path: label_list.txt # 如不使用dataset_dir,则anno_path即为相对于PaddleDetection主目录的相对路径
# dataset_dir: dataset/ppvehicle # 如使用dataset_dir,则dataset_dir/anno_path作为新的anno_path
label_list.txt里的一行记录一个对应种类,如下所示:
vehicle
车牌识别采用Paddle自研超轻量级模型PP-OCRv3_det、PP-OCRv3_rec。在CCPD数据集(CCPD2019+CCPD2020车牌数据集)上进行了fine-tune。模型训练基于PaddleOCR完成,我们提供了预测模型下载:
模型 | 数据集 | 精度 | 下载 | 配置文件 |
---|---|---|---|---|
PP-OCRv3_det | CCPD组合数据集 | hmean:0.979 | 下载链接 | 配置文件 |
PP-OCRv3_rec | CCPD组合数据集 | acc:0.773 | 下载链接 | 配置文件 |
车牌属性采用Paddle自研超轻量级模型PP-LCNet。在VeRi数据集进行训练。模型训练基于PaddleClas完成,我们提供了预测模型下载:
模型 | 数据集 | 精度 | 下载 | 配置文件 |
---|---|---|---|---|
PP-LCNet_x1_0 | VeRi数据集 | 90.81 | 下载链接 | 配置文件 |
@InProceedings{bdd100k,
author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen,
Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
@article{CVIU_UA-DETRAC,
author = {Longyin Wen and Dawei Du and Zhaowei Cai and Zhen Lei and Ming{-}Ching Chang and
Honggang Qi and Jongwoo Lim and Ming{-}Hsuan Yang and Siwei Lyu},
title = {{UA-DETRAC:} {A} New Benchmark and Protocol for Multi-Object Detection and Tracking},
journal = {Computer Vision and Image Understanding},
year = {2020}
}
PaddleDetection是一个基于PaddlePaddle的目标检测端到端开发套件,在提供丰富的模型组件和测试基准的同时,注重端到端的产业落地应用,通过打造产业级特色模型|工具、建设产业应用范例等手段,帮助开发者实现数据准备、模型选型、模型训练、模型部署的全流程打通,快速进行落地应用。
Python Markdown C++ Text Shell other
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