当前位置：首页 > news >正文

YOLO-World 零样本目标检测实战：文本类别提示与视频筛选

news 2026/7/15 0:40:07

YOLO-World 零样本目标检测实战：文本类别提示与视频筛选

这篇教程根据我复现 YOLO-World 零样本目标检测流程时整理，重点演示如何设置文本类别提示、跑通图片推理、再把同一套模型迁移到视频帧上做目标筛选。

YOLO-World 的特点是不用重新训练就能通过文本类别完成目标检测。本文适合想快速验证开放词汇检测效果、并把它接到图片或视频流里的同学。

本文会重点跑通以下流程：

安装 YOLO-World 依赖
准备图片和视频示例
设置文本类别提示并执行检测
通过 NMS 和阈值过滤结果
把同一套检测逻辑应用到视频帧并导出结果

如果你正在系统学习目标检测、实例分割、OCR、多目标跟踪或视觉大模型，建议收藏本文；配套 notebook、示例图片和运行环境说明后续会继续整理。如果环境配置卡住，可以在评论区说明具体报错。

📚 文章目录

YOLO-World 零样本目标检测实战：文本类别提示与视频筛选
- ⚙️ 环境准备
- 🖼️ 准备示例资源
- 🔍 加载 YOLO-World 模型
- 🏷️ 设置文本类别
- 📷 图片目标检测
- 🎞️ 视频检测准备
- 📏 视频过滤与导出
- 📌 小结
- 📚 同系列教程汇总

⚙️ 环境准备

先检查运行环境并安装依赖。建议优先使用带 NVIDIA GPU 的环境，避免推理和训练阶段显存不足。

!nvidia-smi

importos HOME=os.getcwd()print(HOME)

!pip install-q inference-gpu[yolo-world]==0.9.13!pip install-q supervision==0.19.0rc3

!pip install-q supervision==0.19.0rc3

importcv2importsupervisionassvfromtqdmimporttqdmfrominference.modelsimportYOLOWorld

🖼️ 准备示例资源

这一段会准备图片和视频示例。实际复现时，可以直接换成你自己从数据集后台下载的资源。

# 请从数据集后台下载示例图片和视频，并放到当前目录。# 文件名保持为 dog.jpeg 和 yellow-filling.mp4。

SOURCE_IMAGE_PATH=f"{HOME}/dog.jpeg"SOURCE_VIDEO_PATH=f"{HOME}/yellow-filling.mp4"

🔍 加载 YOLO-World 模型

先把零样本模型跑通，确认文本类别提示和推理接口都正常。

model=YOLOWorld(model_id="yolo_world/l")

🏷️ 设置文本类别

YOLO-World 通过文本类别列表来定义要检测的目标。

classes=["person","backpack","dog","eye","nose","ear","tongue"]model.set_classes(classes)

📷 图片目标检测

先在静态图片上验证类别提示和置信度过滤。

image=cv2.imread(SOURCE_IMAGE_PATH)results=model.infer(image)detections=sv.Detections.from_inference(results)

BOUNDING_BOX_ANNOTATOR=sv.BoundingBoxAnnotator(thickness=2)LABEL_ANNOTATOR=sv.LabelAnnotator(text_thickness=2,text_scale=1,text_color=sv.Color.BLACK)

annotated_image=image.copy()annotated_image=BOUNDING_BOX_ANNOTATOR.annotate(annotated_image,detections)annotated_image=LABEL_ANNOTATOR.annotate(annotated_image,detections)sv.plot_image(annotated_image,(10,10))

image=cv2.imread(SOURCE_IMAGE_PATH)results=model.infer(image,confidence=0.003)detections=sv.Detections.from_inference(results)

labels=[f"{classes[class_id]}{confidence:0.3f}"forclass_id,confidenceinzip(detections.class_id,detections.confidence)]annotated_image=image.copy()annotated_image=BOUNDING_BOX_ANNOTATOR.annotate(annotated_image,detections)annotated_image=LABEL_ANNOTATOR.annotate(annotated_image,detections,labels=labels)sv.plot_image(annotated_image,(10,10))

image=cv2.imread(SOURCE_IMAGE_PATH)results=model.infer(image,confidence=0.003)detections=sv.Detections.from_inference(results).with_nms(threshold=0.1)

labels=[f"{classes[class_id]}{confidence:0.3f}"forclass_id,confidenceinzip(detections.class_id,detections.confidence)]annotated_image=image.copy()annotated_image=BOUNDING_BOX_ANNOTATOR.annotate(annotated_image,detections)annotated_image=LABEL_ANNOTATOR.annotate(annotated_image,detections,labels=labels)sv.plot_image(annotated_image,(10,10))

🎞️ 视频检测准备

把同一套模型接到视频帧生成器上，观察连续帧中的检测效果。

generator=sv.get_video_frames_generator(SOURCE_VIDEO_PATH)frame=next(generator)sv.plot_image(frame,(10,10))

classes=["yellow filling"]model.set_classes(classes)

results=model.infer(frame,confidence=0.002)detections=sv.Detections.from_inference(results).with_nms(threshold=0.1)

annotated_image=frame.copy()annotated_image=BOUNDING_BOX_ANNOTATOR.annotate(annotated_image,detections)annotated_image=LABEL_ANNOTATOR.annotate(annotated_image,detections)sv.plot_image(annotated_image,(10,10))

📏 视频过滤与导出

最后对小目标或低置信度结果做过滤，再把标注后的视频写出。

video_info=sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)video_info

width,height=video_info.resolution_wh frame_area=width*height frame_area

results=model.infer(frame,confidence=0.002)detections=sv.Detections.from_inference(results).with_nms(threshold=0.1)detections.area

(detections.area/frame_area)<0.10

detections=detections[(detections.area/frame_area)<0.10]annotated_image=frame.copy()annotated_image=BOUNDING_BOX_ANNOTATOR.annotate(annotated_image,detections)annotated_image=LABEL_ANNOTATOR.annotate(annotated_image,detections)sv.plot_image(annotated_image,(10,10))

TARGET_VIDEO_PATH=f"{HOME}/yellow-filling-output.mp4"

frame_generator=sv.get_video_frames_generator(SOURCE_VIDEO_PATH)video_info=sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)width,height=video_info.resolution_wh frame_area=width*height frame_areawithsv.VideoSink(target_path=TARGET_VIDEO_PATH,video_info=video_info)assink:forframeintqdm(frame_generator,total=video_info.total_frames):results=model.infer(frame,confidence=0.002)detections=sv.Detections.from_inference(results).with_nms(threshold=0.1)detections=detections[(detections.area/frame_area)<0.10]annotated_frame=frame.copy()annotated_frame=BOUNDING_BOX_ANNOTATOR.annotate(annotated_frame,detections)annotated_frame=LABEL_ANNOTATOR.annotate(annotated_frame,detections)sink.write_frame(annotated_frame)