文件列表:

pytorch_YOLO3\.idea\dictionaries\HZQ.xml (84, 2020-07-25)
pytorch_YOLO3\.idea\inspectionProfiles\profiles_settings.xml (228, 2020-07-25)
pytorch_YOLO3\.idea\misc.xml (643, 2020-07-25)
pytorch_YOLO3\.idea\modules.xml (278, 2020-07-25)
pytorch_YOLO3\.idea\pytorch_YOLO3.iml (493, 2020-07-25)
pytorch_YOLO3\.idea\workspace.xml (45359, 2020-08-02)
pytorch_YOLO3\2007_test.txt (0, 2020-07-25)
pytorch_YOLO3\2007_train.txt (1247833, 2020-07-25)
pytorch_YOLO3\2007_val.txt (0, 2020-07-25)
pytorch_YOLO3\get_dr_txt.py (3790, 2020-07-22)
pytorch_YOLO3\get_gt_txt.py (1343, 2020-07-22)
pytorch_YOLO3\get_map.py (34188, 2020-07-22)
pytorch_YOLO3\LICENSE (1065, 2020-07-22)
pytorch_YOLO3\model_data\coco_classes.txt (705, 2020-07-22)
pytorch_YOLO3\model_data\simhei.ttf (9753388, 2020-07-22)
pytorch_YOLO3\model_data\voc_classes.txt (153, 2020-07-22)
pytorch_YOLO3\nets\darknet.py (3202, 2020-07-22)
pytorch_YOLO3\nets\yolo3.py (3046, 2020-07-22)
pytorch_YOLO3\nets\yolo_training.py (16668, 2020-07-22)
pytorch_YOLO3\nets\__pycache__\darknet.cpython-37.pyc (3117, 2020-07-25)
pytorch_YOLO3\nets\__pycache__\yolo3.cpython-37.pyc (2626, 2020-07-25)
pytorch_YOLO3\nets\__pycache__\yolo_training.cpython-37.pyc (10969, 2020-07-25)
pytorch_YOLO3\predict.py (433, 2020-07-22)
pytorch_YOLO3\test.py (485, 2020-07-22)
pytorch_YOLO3\train.py (8981, 2020-07-22)
pytorch_YOLO3\utils\config.py (258, 2020-07-22)
pytorch_YOLO3\utils\dataloader.py (4925, 2020-07-22)
pytorch_YOLO3\utils\utils.py (8013, 2020-07-22)
pytorch_YOLO3\utils\__pycache__\config.cpython-37.pyc (373, 2020-07-25)
pytorch_YOLO3\utils\__pycache__\dataloader.cpython-37.pyc (4560, 2020-07-25)
pytorch_YOLO3\utils\__pycache__\utils.cpython-37.pyc (5854, 2020-07-25)
pytorch_YOLO3\video.py (1029, 2020-07-22)
pytorch_YOLO3\VOCdevkit\VOC2007\ImageSets\Main\test.txt (0, 2020-07-26)
pytorch_YOLO3\VOCdevkit\VOC2007\ImageSets\Main\train.txt (79704, 2020-07-26)
pytorch_YOLO3\VOCdevkit\VOC2007\ImageSets\Main\trainval.txt (79704, 2020-07-26)
pytorch_YOLO3\VOCdevkit\VOC2007\ImageSets\Main\val.txt (0, 2020-07-26)
pytorch_YOLO3\VOCdevkit\VOC2007\voc2yolo3.py (1398, 2020-07-28)
... ...

## YOLOV3:You Only Look Once目标检测模型在Pytorch当中的实现 --- ### 目录 1. [所需环境 Environment](#所需环境) 2. [文件下载 Download](#文件下载) 3. [训练步骤 How2train](#训练步骤) 4. [参考资料 Reference](#Reference) ### 所需环境 torch == 1.2.0 ### 文件下载 训练所需的yolo_weights.pth可以在百度云下载。 链接: https://pan.baidu.com/s/1ncREw6Na9ycZptdxiVMApw 提取码: appk ### 训练步骤 1、本文使用VOC格式进行训练。 2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。 3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。 4、在训练前利用voc2ssd.py文件生成对应的txt。 5、再运行根目录下的voc_annotation.py,运行前需要将classes改成你自己的classes。 ```python classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] ``` 6、就会生成对应的2007_train.txt,每一行对应其图片位置及其真实框的位置。 7、在训练前需要修改model_data里面的voc_classes.txt文件,需要将classes改成你自己的classes。 8、修改utils/config.py里面的NUM_CLASSES与需要训练的种类的个数相同。运行train.py即可开始训练。 ### mAP目标检测精度计算更新 更新了get_gt_txt.py、get_dr_txt.py和get_map.py文件。 get_map文件克隆自https://github.com/Cartucho/mAP 具体mAP计算过程可参考:https://www.bilibili.com/video/BV1zE411u7Vw ### Reference https://github.com/qqwweee/keras-yolo3 https://github.com/eriklindernoren/PyTorch-YOLOv3 https://github.com/BobLiu20/YOLOv3_PyTorch

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