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A mobilenet SSD based face detector, powered by tensorflow object detection api, trained by WIDERFACE dataset.
| Date | Stars |
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| 2026-07-24 | 772 |
| 2026-07-25 | 772 |
| 2026-07-28 | 772 |
| 2026-07-30 | 772 |
| 2026-08-06 | 772 |
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# Tensorflow Face Detector A mobilenet SSD(single shot multibox detector) based face detector with pretrained model provided, powered by tensorflow [object detection api](https://github.com/tensorflow/models/tree/master/research/object_detection), trained by [WIDERFACE dataset](http://shuoyang1213.me/WIDERFACE/). ## Features Speed, run 60fps on a nvidia GTX1080 GPU. Memory, requires less than 364Mb GPU memory for single inference. Robust, adapt to different poses, this feature is credit to [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/), I manually cleaned the dataset to balance the precision and recall trade off. Parallel, multiple process video processing, can inference multiple input simultaneously, I tested to process 4 videos on a single GPU card at the same time, the speed is still competitive, and there's still room to accommodate more processes.  ## Dependencies Tensorflow > 1.2 Tensorflow object detection api (Please follow the official installation instruction, otherwise, I cannot guarantee that you can run the code) OpenCV python ## Usage ### Effect Click [Youtube](https://youtu.be/gw4CVz7SPEs) to view the effect or [Youku](http://v.youku.com/v_show/id_XMzE2MDc0NzcyNA==.html?spm=a2h3j.8428770.3416059.1). ### Prepare pre-trained model Click [here](https://drive.google.com/open?id=0B5ttP5kO_loUdWZWZVVrN2VmWFk) to download the pre-trained model from google drive. Put the model under the model folder. ### Prepare video Put your test video (mp4 format) under the media folder, rename it as test.mp4. ### Run video detection At the source root ```bash python inference_video_face.py ``` After finished the processing, find the output video at media folder. ### Run detection from usb camera You can see how this face detection works with your web camera. ``` usage:inference_usbCam_face.py (cameraID | filename) ``` Here is an example to use usb camera with cameraID=0. ```bash python inference_usbCam_face.py 0 ``` Note: this script does not save video. ### Known Issue Please view that issue [here](https://github.com/yeephycho/tensorflow-face-detection/issues/5) if your output video is blank. A brief reminder is: check the input codec and check the input/output resolution, since this part is irrelevant to the algorithm, no modification will be made to master branch. ### Further The model released by this repo. has already been merged into Deep Video Analytics / Visual Data Network. Please click the following link for more applications. [1] https://www.deepvideoanalytics.com [2] https://github.com/VisualDataNetwork/root ## License Usage of the code and model by yeephycho is under the license of Apache 2.0. The code is based on GOOGLE tensorflow object detection api. Please refer to the license of tensorflow. Dataset is based on WIDERFACE dataset. Please refer to the license to the WIDERFACE license.
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Xiang Yann, Lim · Malaysia
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:875bc191d1c4be78, topic:object-detection, desc:object detection, readme:object detection
matched fp:875bc191d1c4be78, topic:tensorflow