自动驾驶物体检测数据集_Cityscape_yolov5.zip

# Cityscape_yolov5 将Cityscape数据集转化为yolov5格式后，适配街景道路的目标检测任务 ## Datasets: > Cityscape数据集官网下载链接：https://www.cityscapes-dataset.com/news/ > > Cityscape数据集个人网盘链接：链接: https://pan.baidu.com/s/1njtEAV11gs_ZWhtelB1iZw? 提取码: x5kx ## Use: 1. create new dic，解压下载文件到新目录下，并将文件重命名 ```shell mkdir ./cityscape unzip gtFine_trainvaltest.zip -d ./cityscape mv ./gtFine_trainvaltest ./getfine mv ./leftImg8bit_trainvaltest ./leftImg8bit ``` 2. 在当前目录下进行数据处理 ```shell mv handle.py ./cityscape python handle.py ``` 3. 生成images和labels目录 4. 提取labels中的class的顺序 (Recommend) * __由于生成的class顺序不一定一样，推荐用以下方法读取自己的顺序__ ```python path = './cityscape/labels/classes.txt' tmp = [] with open(path, "r") as f: for _ in f: tmp.append(_.replace('\n','')) print(tmp) ``` ```shell ['rectification border', 'road', 'sidewalk', ··· , 'ridergroup', 'truckgroup'] # class names ``` 5. 创建cityscape.yaml并写入以下内容 ```shell path: ../cityscape # dataset root dir train: images/train # train images (relative to 'path') 118287 images val: images/val # val images (relative to 'path') 5000 images test: images/test # Classes nc: 38 # number of classes names: ['rectification border', 'road', 'sidewalk', ··· , 'ridergroup', 'truckgroup'] # class names #把自己上面生成的复制进来！ ``` 6. 将cityscape.yaml复制到./yolov5/data中 至此，完成数据集的准备工作 7. Benchmark __mAP = 42.25(Full classes)__