---
title: cv_nanodet_face-human-hand-detection
canonical_url: "https://www.modelscope.cn/models/damo/cv_nanodet_face-human-hand-detection"
md_url: "https://www.modelscope.cn/models/damo/cv_nanodet_face-human-hand-detection.md"
repository: damo/cv_nanodet_face-human-hand-detection
chinese_name: "目标检测-人脸人体人手-通用领域"
last_updated: 2023-11-16
license: "Apache License 2.0"
pipeline_tag: face-human-hand-detection
tasks:
  - face-human-hand-detection
model_type:
  - NanoDet
library_name:
  - pytorch
frameworks:
  - pytorch
domain:
  - cv
downloads: 1132039
stars: 42
tags:
  - "通用目标检测"
---

# cv_nanodet_face-human-hand-detection

> cv_nanodet_face-human-hand-detection - damo 在 ModelScope 开源的模型。通用场景下的，人脸-人体-人手三合一目标检测

damo/cv_nanodet_face-human-hand-detection 是 ModelScope 魔搭社区上的face-human-hand-detection模型，采用 Apache License 2.0 许可。

- **Repository**: damo/cv_nanodet_face-human-hand-detection
- **License**: Apache License 2.0
- **Tasks**: face-human-hand-detection
- **Tags**: 通用目标检测
- **Downloads**: 1132039
- **Stars**: 42
- **Last updated**: 2023-11-16

Source: https://www.modelscope.cn/models/damo/cv_nanodet_face-human-hand-detection

---

# 目标检测-人脸人体人手-通用领域

这是一个人脸、人体、人手三合一检测模型

## 模型描述

利用[NanoDet](https://github.com/RangiLyu/nanodet)，进行人脸、人体、人手的检测

## 使用方式和范围


### 如何使用

在ModelScope框架上，提供图片，得到识别的结果

#### 代码范例
```python
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

face_human_hand_detection = pipeline(Tasks.face_human_hand_detection, model='damo/cv_nanodet_face-human-hand-detection')
result_status = face_human_hand_detection('data/test/images/face_human_hand_detection.jpg')
labels = result_status[OutputKeys.LABELS]
boxes = result_status[OutputKeys.BOXES]
scores = result_status[OutputKeys.SCORES]

```

输出结果示例如下：

labels = [2, 1, 0]

boxes = [[78, 282, 240, 504], [127, 87, 332, 370], [0, 0, 367, 639]]

scores = [0.8202137351036072, 0.8987470269203186, 0.9679114818572998]

labels为类别，0代表人体，1代表人脸，2代表人手，

boxes为和labels对应的检测框的坐标，中间4个数字代表检测框的坐标，分别代表左上角的x，左上角的y，右下角的x，右下角的y

scores为对应的置信度分数


### 引用
```BibTeX
@misc{=nanodet,
    title={NanoDet-Plus: Super fast and high accuracy lightweight anchor-free object detection model.},
    author={RangiLyu},
    howpublished = {\url{https://github.com/RangiLyu/nanodet}},
    year={2021}
}
```