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output │ ├── airport_output.avi│ ├── car_chase_01_output.avi│ ├── car_chase_02_output.avi│ └── overpass_output.avi videos │ ├── airport.mp│ ├── car_chase_0mp│ ├── car_chase_0mp│ └── overpass.mp# yolo-coco│ ├── coco.names│ ├── yolovcfg...
videos：包含四个示例视频，如airport.mp4、car_chase_0mp4等，这些视频将用于测试YOLO对象检测器的实时处理能力。output：此文件夹将用于存放经过YOLO处理并带有边界框和类名称注释的视频文件，例如airport_output.avi等。yolo.py和yolo_video.py：这两个Python脚本分别用于执行静态图像和视频中的对象检测任务。此外，还...
│ ├── airport.mp4 │ ├── car_chase_01.mp4 │ ├── car_chase_02.mp4 │ └── overpass.mp4 ├── yolo-coco │ ├── coco.names │ ├── yolov3.cfg │ └── yolov3.weights ├── yolo.py └── yolo_video...
类别:'plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court', 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter','container crane','airport','helipad' 包括 11...
卫星图片地面目标识别检测数据集 1500张 yolo数据集 已增强 标注名称以及数量: Expressway-Service-area : 143 Expressway-toll-station : 57 airplane : 301 airport : 132 baseballfield : 323 basketballcourt …
7: airport 4. 训练模型 使用YOLOv8进行目标检测任务训练,可以使用以下命令: bash深色版本 yolo detect train data=ai_tod.yaml model=yolov8n.pt epochs=100 imgsz=512 解释: data=ai_tod.yaml: 指定数据配置文件。 model=yolov8n.pt: 使用预训练的YOLOv8检测模型(yolov8n)。你可以选择其他大小的模型,如...
if__name__=='__main__':img_path="./UI_rec/test_/airport_136_jpg"image=cv_imread(img_path)image=cv2.resize(image,(850,500))img0=image.copy()img=letterbox(img0,new_shape=imgsz)[0]img=np.stack(img,0)img=img[:,:,::-1].transpose(2,0,1)# BGR to RGB, to 3x416x416img=np...
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│ ├── airport.mp4 │ ├── car_chase_01.mp4 │ ├── car_chase_02.mp4 │ └── overpass.mp4 ├── yolo-coco │ ├── coco.names │ ├── yolov3.cfg │ └── yolov3.weights ├── yolo.py └── yolo_video...