VisDrone-Dataset1.类别# 类别数量 nc: 10 # 类别名称必须与类别ID对应 names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor2.Visdrone.yaml# Ultralytics YOLO , AGPL-3.0 license # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics # Documentation: https://docs.ultralytics.com/datasets/detect/coco8/ # Example usage: yolo train datacoco8.yaml # parent # ├── ultralytics # └── datasets # └── coco8 ← downloads here (1 MB) # 数据集路径相对于此文件或绝对路径 path: ../datasets/visdrone2019 # dataset root dir # 训练集和验证集路径相对于path train: ../train/images # train images (relative to path) 4 images val: ../valid/images # val images (relative to path) 4 images # test: ../test/images # test images (optional) # 类别数量 nc: 10 # 类别名称必须与类别ID对应 names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor3.YOLO格式数据集转换创建test.pyimport os from pathlib import Path import argparse def visdrone2yolo(dir): from PIL import Image from tqdm import tqdm def convert_box(size, box): # Convert VisDrone box to YOLO xywh box dw 1. / size[0] dh 1. / size[1] return (box[0] box[2] / 2) * dw, (box[1] box[3] / 2) * dh, box[2] * dw, box[3] * dh (dir / labels).mkdir(parentsTrue, exist_okTrue) # make labels directory pbar tqdm((dir / annotations).glob(*.txt), descfConverting {dir}) for f in pbar: img_size Image.open((dir / images / f.name).with_suffix(.jpg)).size lines [] with open(f, r) as file: # read annotation.txt for row in [x.split(,) for x in file.read().strip().splitlines()]: if row[4] 0: # VisDrone ignored regions class 0 continue cls int(row[5]) - 1 # 类别号-1 box convert_box(img_size, tuple(map(int, row[:4]))) lines.append(f{cls} { .join(f{x:.6f} for x in box)}\n) with open(str(f).replace(os.sep annotations os.sep, os.sep labels os.sep), w) as fl: fl.writelines(lines) # write label.txt if __name__ __main__: # Create an argument parser to handle command-line arguments parser argparse.ArgumentParser() parser.add_argument(--dir_path, typestr, defaultr.\visdrone2019, helpvisdrone数据集路径) args parser.parse_args() dir Path(args.dir_path) # Convert for d in VisDrone2019-DET-train, VisDrone2019-DET-val, VisDrone2019-DET-test-dev: visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels4.数据集配置文件yolov11.yaml# Ultralytics YOLO , AGPL-3.0 license # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics # Documentation: https://docs.ultralytics.com/datasets/detect/coco8/ # Example usage: yolo train datacoco8.yaml # parent # ├── ultralytics # └── datasets # └── coco8 ← downloads here (1 MB) # 数据集路径相对于此文件或绝对路径 path: ../datasets/visdrone2019 # dataset root dir # 训练集和验证集路径相对于path train: ../train/images # train images (relative to path) 4 images val: ../valid/images # val images (relative to path) 4 images # test: ../test/images # test images (optional) # 类别数量 nc: 10 # 类别名称必须与类别ID对应 names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor参考文章【超详细】基于YOLOv8训练无人机视角Visdrone2019数据集_visdrone数据集-CSDN博客GitHub - VisDrone/VisDrone-Dataset: The dataset for drone based detection and tracking is released, including both image/video, and annotations. · GitHub
VisDrone-Dataset1.类别# 类别数量 nc: 10 # 类别名称必须与类别ID对应 names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor2.Visdrone.yaml# Ultralytics YOLO , AGPL-3.0 license # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics # Documentation: https://docs.ultralytics.com/datasets/detect/coco8/ # Example usage: yolo train datacoco8.yaml # parent # ├── ultralytics # └── datasets # └── coco8 ← downloads here (1 MB) # 数据集路径相对于此文件或绝对路径 path: ../datasets/visdrone2019 # dataset root dir # 训练集和验证集路径相对于path train: ../train/images # train images (relative to path) 4 images val: ../valid/images # val images (relative to path) 4 images # test: ../test/images # test images (optional) # 类别数量 nc: 10 # 类别名称必须与类别ID对应 names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor3.YOLO格式数据集转换创建test.pyimport os from pathlib import Path import argparse def visdrone2yolo(dir): from PIL import Image from tqdm import tqdm def convert_box(size, box): # Convert VisDrone box to YOLO xywh box dw 1. / size[0] dh 1. / size[1] return (box[0] box[2] / 2) * dw, (box[1] box[3] / 2) * dh, box[2] * dw, box[3] * dh (dir / labels).mkdir(parentsTrue, exist_okTrue) # make labels directory pbar tqdm((dir / annotations).glob(*.txt), descfConverting {dir}) for f in pbar: img_size Image.open((dir / images / f.name).with_suffix(.jpg)).size lines [] with open(f, r) as file: # read annotation.txt for row in [x.split(,) for x in file.read().strip().splitlines()]: if row[4] 0: # VisDrone ignored regions class 0 continue cls int(row[5]) - 1 # 类别号-1 box convert_box(img_size, tuple(map(int, row[:4]))) lines.append(f{cls} { .join(f{x:.6f} for x in box)}\n) with open(str(f).replace(os.sep annotations os.sep, os.sep labels os.sep), w) as fl: fl.writelines(lines) # write label.txt if __name__ __main__: # Create an argument parser to handle command-line arguments parser argparse.ArgumentParser() parser.add_argument(--dir_path, typestr, defaultr.\visdrone2019, helpvisdrone数据集路径) args parser.parse_args() dir Path(args.dir_path) # Convert for d in VisDrone2019-DET-train, VisDrone2019-DET-val, VisDrone2019-DET-test-dev: visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels4.数据集配置文件yolov11.yaml# Ultralytics YOLO , AGPL-3.0 license # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics # Documentation: https://docs.ultralytics.com/datasets/detect/coco8/ # Example usage: yolo train datacoco8.yaml # parent # ├── ultralytics # └── datasets # └── coco8 ← downloads here (1 MB) # 数据集路径相对于此文件或绝对路径 path: ../datasets/visdrone2019 # dataset root dir # 训练集和验证集路径相对于path train: ../train/images # train images (relative to path) 4 images val: ../valid/images # val images (relative to path) 4 images # test: ../test/images # test images (optional) # 类别数量 nc: 10 # 类别名称必须与类别ID对应 names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor参考文章【超详细】基于YOLOv8训练无人机视角Visdrone2019数据集_visdrone数据集-CSDN博客GitHub - VisDrone/VisDrone-Dataset: The dataset for drone based detection and tracking is released, including both image/video, and annotations. · GitHub
