基于MindSpore的YOLOv3-DarkNet53篮球检测项目python源码.zip

# MindSpore YOLOv3-DarkNet53篮球检测教程指导（GPU环境） 该教程旨在指导大家使用GPU资源完成MindSpore YOLOv3-DarkNet53篮球检测的教程。 > **注意：** 该教程的代码是基于`v1.0`版本的MindSpore [ModelZoo](https://github.com/mindspore-ai/mindspore/tree/r1.0/model_zoo/official/cv/yolov3_darknet53)开发完成的。 > **注意：** 考虑到预训练过程会占用大量时间，本次课程我们不会提供完整的数据集用于模型训练，但我们会提供YOLOv3预训练模型以及测试数据集，方便大家用于模型验证和推理工作。 ## 上手指导 ### 安装系统库 * 系统库 ``` sudo apt install -y unzip ``` * Python库 ``` pip install opencv-python pycocotools ``` * MindSpore (**v1.0**) MindSpore的安装教程请移步至 [MindSpore安装页面](https://www.mindspore.cn/install). ### 数据准备 * 下载测试数据集（验证任务使用） ``` cd basketball-dataset/ && wget https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/yolov3-darknet53/basketball-dataset/basketball-dataset.zip unzip basketball-dataset.zip && rm basketball-dataset.zip ``` 或者您可以直接点击 [https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/yolov3_darknet53/basketball-dataset/basketball-dataset.zip](https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/yolov3_darknet53/basketball-dataset/basketball-dataset.zip) 从浏览器中下载该数据集，手动解压。 * 下载YOLOv3-DarkNet53预训练模型（验证/推理任务使用） ``` cd ../resnet_gpu/ckpt_files && wget https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/yolov3_darknet53/ckpt_files/yolov3-320_168000.ckpt ``` 或者您可以直接点击 [https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/yolov3_darknet53/ckpt_files/yolov3-320_168000.ckpt](https://ascend-tutorials.obs.cn-north-4.myhuaweicloud.com/yolov3_darknet53/ckpt_files/yolov3-320_168000.ckpt) 从浏览器中下载预训练模型。 ### 模型验证 ``` python eval.py --data_dir ../basketball-dataset/ --pretrained ./ckpt_files/yolov3-320_168000.ckpt ``` ``` =============coco eval result========= Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.829 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.716 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.550 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.339 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.645 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.646 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.600 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.646 ``` ### 模型推理 ``` python predict.py --image_path ./00086.jpg --pretrained ./ckpt_files/yolov3-320_168000.ckpt ``` 输入图片： <img src="../docs/00086.jpg" alt="Input Image" width="600"/> 输出结果： <img src="../docs/output.jpg" alt="Output Image" width="600"/> ## 许可证 [Apache License 2.0](../../LICENSE)