- PyramidBox is a one-stage face detector based on SSD. It can redict results across six scale levels of feature maps. This module is based on PyramidBox, trained on WIDER FACE Dataset, and supports face detection.
## II.Installation
...
...
@@ -73,7 +73,7 @@
score_thresh=0.15)
```
- 检测输入图片中的所有人脸位置.
- Detect all faces in image
- **Parameters**
...
...
@@ -82,20 +82,20 @@
- use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**
- output_dir (str): save path of images;
- visualization (bool): Whether to save the results as picture files;
- score_thresh (float): 置信度的阈值.
- score_thresh (float): the confidence threshold
**NOTE:** choose one parameter to provide data from paths and images
- **Return**
- res (list\[dict\]): classication results, each element in the list is dict, key is the label name, and value is the corresponding probability
- path (str): 原输入图片的路径
- data (list): 检测结果，list的每一个元素为 dict，各字段为:
- confidence (float): 识别的置信度
- left (int): 边界框的左上角x坐标
- top (int): 边界框的左上角y坐标
- right (int): 边界框的右下角x坐标
- bottom (int): 边界框的右下角y坐标
- res (list\[dict\]): results
- path (str): path for input image
- data (list): detection results, each element in the list is dict
- confidence (float): the confidence of the result
- left (int): the upper left corner x coordinate of the detection box
- top (int): the upper left corner y coordinate of the detection box
- right (int): the lower right corner x coordinate of the detection box
- bottom (int): the lower right corner y coordinate of the detection box
- PyramidBox-Lite is a light-weight model based on PyramidBox proposed by Baidu in ECCV 2018. This model has solid robustness against interferences such as light and scale variation. This module is optimized for mobile device, based on PyramidBox, trained on WIDER FACE Dataset and Baidu Face Dataset, and can be used for face detection.
## II.Installation
...
...
@@ -73,7 +73,7 @@
confs_threshold=0.6)
```
- 检测输入图片中的所有人脸位置.
- Detect all faces in image
- **Parameters**
...
...
@@ -82,21 +82,21 @@
- use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**
- output_dir (str): save path of images;
- visualization (bool): Whether to save the results as picture files;
- PyramidBox-Lite is a light-weight model based on PyramidBox proposed by Baidu in ECCV 2018. This model has solid robustness against interferences such as light and scale variation. This module is optimized for mobile device, based on PyramidBox, trained on WIDER FACE Dataset and Baidu Face Dataset, and can be used for mask detection.
- The model downloaded from paddlehub is a prediction model. If we want to deploy it in mobile device, we can use OPT tool provided by PaddleLite to transform the model. For more information, please refer to [OPT tool](https://paddle-lite.readthedocs.io/zh/latest/user_guides/model_optimize_tool.html))
- PyramidBox-Lite is a light-weight model based on PyramidBox proposed by Baidu in ECCV 2018. This model has solid robustness against interferences such as light and scale variation. This module is based on PyramidBox, trained on WIDER FACE Dataset and Baidu Face Dataset, and can be used for face detection.
## II.Installation
...
...
@@ -73,7 +74,7 @@
confs_threshold=0.6)
```
- 检测输入图片中的所有人脸位置.
- Detect all faces in image
- **Parameters**
...
...
@@ -82,21 +83,21 @@
- use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**
- output_dir (str): save path of images;
- visualization (bool): Whether to save the results as picture files;
- PyramidBox-Lite is a light-weight model based on PyramidBox proposed by Baidu in ECCV 2018. This model has solid robustness against interferences such as light and scale variation. This module is based on PyramidBox, trained on WIDER FACE Dataset and Baidu Face Dataset, and can be used for mask detection.
- The model downloaded from paddlehub is a prediction model. If we want to deploy it in mobile device, we can use OPT tool provided by PaddleLite to transform the model. For more information, please refer to [OPT tool](https://paddle-lite.readthedocs.io/zh/latest/user_guides/model_optimize_tool.html))
- ### Deploy the model with Paddle Lite
- Please refer to[Paddle-Lite mask detection model deployment demo](https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite/demo/cxx)
- Ultra-Light-Fast-Generic-Face-Detector-1MB is an extreme light-weight model for real-time face detection in low computation power devices. This module is based on Ultra-Light-Fast-Generic-Face-Detector-1MB, trained on WIDER FACEDataset, and can be used for face detection.
## II.Installation
...
...
@@ -73,7 +73,7 @@
confs_threshold=0.5)
```
- 检测输入图片中的所有人脸位置.
- Detect all faces in image
- **Parameters**
...
...
@@ -83,21 +83,21 @@
- use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**
- output_dir (str): save path of images;
- visualization (bool): Whether to save the results as picture files;
- confs\_threshold (float): 置信度的阈值.
- confs\_threshold (float): the confidence threshold
**NOTE:** choose one parameter to provide data from paths and images
- **Return**
- res (list\[dict\]): classication results, each element in the list is dict, key is the label name, and value is the corresponding probability
- Ultra-Light-Fast-Generic-Face-Detector-1MB is an extreme light-weight model for real-time face detection in low computation power devices. This module is based on Ultra-Light-Fast-Generic-Face-Detector-1MB, trained on WIDER FACEDataset, and can be used for face detection.
## II.Installation
...
...
@@ -73,7 +73,7 @@
confs_threshold=0.5)
```
- 检测输入图片中的所有人脸位置.
- Detect all faces in image
- **Parameters**
...
...
@@ -83,21 +83,21 @@
- use_gpu (bool): use GPU or not; **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**
- output_dir (str): save path of images;
- visualization (bool): Whether to save the results as picture files;
- confs\_threshold (float): 置信度的阈值.
- confs\_threshold (float): the confidence threshold
**NOTE:** choose one parameter to provide data from paths and images
- **Return**
- res (list\[dict\]): classication results, each element in the list is dict, key is the label name, and value is the corresponding probability
