# Object Detection with Yolo

*author: shiyu22*



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### Description

**Object Detection** is a computer vision technique that locates and identifies people, items, or other objects in an image. Object detection has applications in many areas of computer vision, including image retrieval, image annotation, vehicle counting, object tracking, etc.

This operator uses [PyTorch.yolov5](https://pytorch.org/hub/ultralytics_yolov5/) to detect the object.



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### Code Example



Writing the pipeline in the simplified way

```Python
from towhee.dc2 import pipe, ops

p = (
    pipe.input('url')
        .map('url', 'img', ops.image_decode.cv2_rgb())
        .flat_map('img', ('boxes', 'class', 'score'), ops.object_detection.yolo())
        .output('class', 'score')
    )

data = 'https://towhee.io/object-detection/yolov5/raw/branch/main/test.png'
res = p(data).get()
```

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## Factory Constructor

Create the operator via the following factory method

***object_detection.yolov5()***



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### Interface

The operator takes an image as input. It first detects the objects appeared in the image, and gives the bounding box of each object.

**Parameters:**

​	**img**: numpy.ndarray

​	Image data in ndarray format.



**Return**: List[List[(int, int, int, int)], ...], List[str], List[float]]

The return value is a tuple of (boxes, classes, scores). The *boxes* is a list of bounding boxes. Each bounding box is represented by the top-left and the bottom right points, i.e. (x1, y1, x2, y2). The *classes* is a list of prediction labels. The *scores* is a list of the confidence scores.