task: detect # (str) YOLO task, i.e. detect, segment, classify, pose mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark 在/root/ultralytics/runs/detect下会生成predict文件,生成结果如图: 4.参数解读 4.1 detect.py(推理) weights: 模型权重路径,即指定使用...
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) ...
yolo task=detect mode=predict model=runs/detect/train2/weights/best.pt conf=0.25 source="Dataset/images/test" save_txt=True save=True image 495/500 /home/forlinx/torch_venv/Dataset/images/test/hard_hat_workers97.png: 640x640 8 helmets, 2.2ms image 496/500 /home/forlinx/torch_venv/Dataset...
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...
img.save(img_path) #可以保存预测结果以及可视化图片 result = model.predict(source=img_path, save=True, save_txt=True, conf = 0.3) from pathlib import Path import ultralytics from ultralytics.data import utils yaml_path = str(Path(ultralytics.__file__).parent/'cfg/datasets/coco128-seg....
(bool) save results to JSON filesave_hybrid:False# (bool) save (labels + additional predictions)conf:0.25# (float, optional) (default 0.25 predict, 0.001 val)iou:0.5# (float) intersection over union (IoU) threshold for NMSmax_det:300# (int) maximum number of detections per imagehalf:...
results_list = model.predict(source=[image_file1, image_file2], show=False, save=True, save_conf=True, save_txt=True) for results in results_list: boxes = results.boxes speed = results.speed names = results.names json = results.tojson() ...
Predict YOLO 模型的预测设置包含一系列超参数和配置,这些超参数和配置会影响模型在推理新数据期间的性能、速度和准确性。 仔细调整和试验这些设置对于实现特定任务的最佳性能至关重要。 关键设置包括置信度阈值、非极大值抑制 (NMS) 阈值和考虑的类别数量。 影响预测过程的其他因素包括输入数据的大小和格式、是否存在补...
predict-G.jpeg pyproject.toml roxml_to_dota.py splitdata.py test.jpeg train-obb.py viewtxt.py yolov8-obb-json.py yolov8-obb-roi.py Repository files navigation README AGPL-3.0 license 中文|한국어|日本語|Русский|Deutsch|Français|Español|Português|हिन्दी|...
Predict from ultralytics import YOLO # Load a pretrained YOLOv8n model model = YOLO('yolov8n.pt') # Run inference on 'bus.jpg' with arguments model.predict('https://ultralytics.com/images/bus.jpg', save=True, imgsz=320, conf=0.5) Validate yolo detect val model=path/to/best.pt ...