PyramidBox

This is an unofficial Tensorflow re-implementation of PyramidBox: A Context-assisted Single Shot Face Detector, which achieves superior performance among the state-of-the-art on the two common face detection benchmarks, FDDB and WIDER FACE.

Note

There is still a gap in performance from the paper. May be caused by several reasons:

• Without implementing data-anchor-sampling.
• Differences of data augmentation from original.
• The batch size in the paper is 16, but I used 1 because of the limitation of memory.
• Hyperparameters not mentioned in the paper.
• Differences of deep learning framework.

Results

Face Detection

Results on WIDER FACE validation set:

This is just a very casual training result. I believe you can achieve better results after trying some other hyperparameters. For example: batch size, learning rate and some parameters related to the loss function,etc.

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| Method | AP Easy | AP Medium | AP Hard | |:-------|:-------:|:-------:|:-------: | original | 96.1 | 95.0 | 88.9 | | this repo | 90.6 | 88.8 | 73.4 |

</center>

Usage

Prerequisites

(Only tested on) Ubuntu 16.04 with:

• Python3
• Tensorflow-gpu 1.4
• OpenCV3

Clone the repo

git clone https://github.com/EricZgw/PyramidBox.git
python makedir.py

Download PyramidBox models form BaiduYun or GoogleDrive .

Demo

Run the following script for visualization:

python demo.py

Train on WIDER FACE Datasets

1. Download pre-trained VGG16 models from here and put it to /checkpoints. <br>
2. Download WIDER FACE Datasets and convert to VOC format. Path looks like below:

datasets/
       |->widerface/
       |    |->WIDER_train/
       |    |->WIDER_val/
       |    |->WIDER_test/
       |    |->Annotations/
       |    |->JPEGImages/
       |    |...

3. Run the following script to generate TFRecords:

python datasets/pascalvoc_to_tfrecords.py
You can run `check_data_io.py` to check data. This step is not necessary.

4. The training strategy is two-stages: First run train_model.py with below setting to train additional PyramidBox layers:

self.fine_tune_vgg16 = False

5. Then set self.fine_tune_vgg16 =Ture to run train_model.py to train total network.

Validation

Run the following script for evaluation and get mAP:

python widerface_eval.py
cd eval/eval_tools
octave wider_eval.m

TODO

• Add data-anchor-sampling
• Try more logical and rigorous data augmentation
• Transfer to other backbone networks

Reference

SSD-Tensorflow<br> SSD_tensorflow_VOC

Contact

If you find any problems, welcome to open a new issue or contact zhaogw93@126.com .