results = model.predict( r"D:\ultralytics-main\ultralytics\datasets\original-license-plates\test\images\b9f5b9acf1777acf_jpg.rf.b92969d5c3738ece6a84dcd2d0ea3ce0.jpg", save=True, imgsz=320, conf=0.5,device=0,iou=0.5) 3.2 预测结果 当使用save=true,训练后的结果保存在 runs\detect\predi...
https://docs.ultralytics.com/modes/predict/#inference-arguments。 使用CLI预测 运行以下CLI命令即可启动模型: 复制 python3 predict.py 通过实时网络摄像头馈送运行YOLOv8模型预测 CLI命令 使用CLI方式进行预测应用的命令是: 复制 yolo detect predict model=best.pt source=0 show=True cnotallow=0.25 save=Tru...
使用yolo.exe 推理的代码: yolo predict model=D:\my_workspace\py_code\yolo8\Scripts\yolov8m.pt source=D:\my_workspace\source\opencv\yolov8\WinFormsApp1\bus.jpg yolo predict model=D:\my_workspace\source\opencv\yolov8\WinFormsApp1\yolov8m.onnx source=D:\my_workspace\source\opencv\yolov8\...
2MB)Exportcomplete (3.2s)Resultssaved toI:\yolov8\Yolov8_for_PyTorchPredict: yolo predict task=detect model=yolov8n.onnximgsz=640Validate: yolo val task=detect model=yolov8n.onnximgsz=640data=coco.yamlVisualize:https://netron.app从输出信息中可以看出， yolov8n.pt原始模型的输出尺寸为 ...
add_argument('--save_path', default="../yolo_model", type=str) parser.add_argument('--train_path', default="./resource/all_voc_train.txt") parser.add_argument('--eval_path', default="./resource/all_voc_test.txt") parser.add_argument('--model_path', default=None) parser.add_...
e. 'val', 'test' or 'train' save_json: False # save results to JSON file save_hybrid: False # save hybrid version of labels (labels + additional predictions) conf: # object confidence threshold for detection (default 0.25 predict, 0.001 val) iou: 0.7 # intersection over union (IoU) ...
if not os.path.exists(txtsavepath): os.makedirs(txtsavepath) num = len(total_xml) list_index = range(num) tv = int(num * trainval_percent) tr = int(tv * train_percent) trainval = random.sample(list_index, tv) train = random.sample(trainval, tr) ...
Predict: yolo predict task=detect model=yolov8n.onnx imgsz=640Validate: yolo val task=detect model=yolov8n.onnx imgsz=640data=coco.yaml Visualize: https://netron.app 从输出信息中可以看出, yolov8n.pt原始模型的输出尺寸为 (1, 3, 640, 640),格式为 BCHW ,输出尺寸为 (1, 84, 8400) 。
mode: 选择是训练、验证还是预测的任务蕾西 可选['train', 'val', 'predict'] model: 选择yolov8不同的模型配置文件,可选yolov8s.yaml、yolov8m.yaml、yolov8l.yaml、yolov8x.yaml data: 选择生成的数据集配置文件 epochs:指的就是训练过程中整个数据集将被...
predict(source=videoPath, save=True) 代码语言:javascript 复制 import subprocess # Convert AVI to MP4 using FFmpeg subprocess.call(['ffmpeg', '-y', '-loglevel', 'panic', '-i', '/content/runs/segment/predict/sample_video.avi', 'output_video.mp4']) from IPython.display import Video # ...