文件列表:

LICENSE (1067, 2019-03-26)
__init__.py (91, 2019-03-26)
check_point (0, 2019-03-26)
data (0, 2019-03-26)
demo (0, 2019-03-26)
demo\demo.jpg (149030, 2019-03-26)
demo\demo2.jpg (74716, 2019-03-26)
demo\demo3.jpg (131740, 2019-03-26)
demo\demo4.jpg (107548, 2019-03-26)
eval.py (6134, 2019-03-26)
model (0, 2019-03-26)
model\Makefile (57, 2019-03-26)
model\SSH.py (8965, 2019-03-26)
model\__init__.py (32, 2019-03-26)
model\dataset (0, 2019-03-26)
model\dataset\__init__.py (0, 2019-03-26)
model\dataset\factory.py (412, 2019-03-26)
model\dataset\imdb.py (4622, 2019-03-26)
model\dataset\wider.py (6600, 2019-03-26)
model\network.py (5189, 2019-03-26)
model\nms (0, 2019-03-26)
model\nms\__init__.py (0, 2019-03-26)
model\nms\cpu_nms.c (368798, 2019-03-26)
model\nms\cpu_nms.cpython-35m-x86_64-linux-gnu.so (230944, 2019-03-26)
model\nms\cpu_nms.pyx (2241, 2019-03-26)
model\nms\gpu_nms.cpp (345269, 2019-03-26)
model\nms\gpu_nms.cpython-35m-x86_64-linux-gnu.so (240648, 2019-03-26)
model\nms\gpu_nms.hpp (147, 2019-03-26)
model\nms\gpu_nms.pyx (1109, 2019-03-26)
model\nms\nms_kernel.cu (5525, 2019-03-26)
model\nms\nms_wrapper.py (657, 2019-03-26)
model\nms\py_cpu_nms.py (1051, 2019-03-26)
model\roi_data_layer (0, 2019-03-26)
model\roi_data_layer\__init__.py (0, 2019-03-26)
model\roi_data_layer\layer.py (2279, 2019-03-26)
... ...

# A pytorch implementation of SSH (Single shot headless) face detector [](https://doi.org/10.5281/zenodo.1728203) ## Introduction This project is a pytorch implementation of a Single shot headless face detector. Original paper can be find here [**SSH face detector ICCV 2017 paper**](https://arxiv.org/abs/1708.03979) official implementation is in Caffe [**SSH/mahyarnajibi**](https://github.com/mahyarnajibi/SSH) During my implementation of pytorch version, I referred the Faster-RCNN pytorch implementation [jwyang/faster-rcnn.pytorch](). Compare to other faster-rcnn pytorch implementation, jwyang's implementation revised all layers from numpy to pytorch. I used anchor_target_layer , generate_anchor_box, propsal_layer and bbox layer for his implementation. ### What i included in this repository: - [x] Support python3 - [x] Support pytorch-1.0 - [x] Support distributed training (gradient average) - [ ] Support multi-gpu(Multi-GPU can be done by using mulitiple distributed training on a single machine. The reason it is not support right now is because of the input size is not a fixed size.) - [x] Match mAP with original paper (current pretrain model have lower mAP (0.88)in wider face dataset that official implementation (0.907). ~~Maybe more training is needed. Great thanks if anyone can help me find out what went wrong)~~ i fixed a problem in anchor_target_layer. Now i can get( 0.905, 0.890, 0.809) vs SSH:(0.919,0.907,0.809) ### Installation 1. Clone this repository 2. Create conda env by using pytorch.yml provide in this repository 3. Run ```make``` in the ```model``` directory: ``` cd model make ``` ### Pretrained model download pretrained model by [this link](https://drive.google.com/file/d/19bmuol6CbSqL3pj9SBzUL6UhrxC3XYbC/view?usp=sharing) place file under check_point/ folder ### Training a model For training on the *WIDER* dataset, you need to download the [WIDER face training images](https://drive.google.com/file/d/0B6eKvaijfFUDQUUwd21EckhUbWs/view?usp=sharing) and the [face annotations](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/bbx_annotation/wider_face_split.zip) from the [dataset website](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/). These files should be copied into ```data/datasets/wider/``` (you can create symbolic links if you prefer to store the actual data somewhere else). Before starting to train you should have a directory structure as follows: ``` data |--datasets |--wider |--WIDER_train/ |--wider_face_split/ |--imagenet_models |--VGG16.caffemodel ``` For training with the default parameters, you can call the ```train.py```. As an example: ``` python3 train.py ``` For a list of all possible options run ```python3 train.py --help```. ### Training on distributed system (Or multi GPU) On each node, you can run ```train_dist.py``` For example ``` python3 train_dist.py --master_ip xxx.xxx.xxx.xxx --master_port xxxxx --rank 0 --world_size 2 ``` --master_ip is your master node ip address and also --master_port. You need to make sure that each node would able to communicate with master node via TCP/IP. Rank 0 means master node, rank range from[0,world_size). World_size define total number of node you want to use. You can treat a node with multi GPU as a multi node with single gpu. by doing this, we can utilize multi GPU on a single machine. For example ``` python train_dist.py --gpu='0' --rank=0 --world_size=2 --master_ip='127.0.0.1' --master_port=29500 python train_dist.py --gpu='1' --rank=1 --world_size=2 --master_ip='127.0.0.1' --master_port=29500 ``` ### Test a trained model For test a image with trained model, you can call the ```test.py```. As an example: ``` python3 test.py ``` For a list of all possible options run ```python3 train.py --help```. ### Evaluating a trained model The evaluation on the *WIDER* dataset is based on the official *WIDER* evaluation tool which requires *MATLAB*. you need to download the [validation images](https://drive.google.com/file/d/0B6eKvaijfFUDd3dIRmpvSk8tLUk/view?usp=sharing) and the [annotations](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/bbx_annotation/wider_face_split.zip) (if not downloaded for training) from the *WIDER* [dataset website](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/). These files should be copied into the ```data/datasets/wider``` directory as follows: ``` data |--datasets |--wider |--WIDER_val/ |--wider_face_split/ ``` For evaluating with the default parameters, you can call the ```eval.py```. As an example: ``` python3 eval.py ``` For a list of all possible options run ```python3 eval.py --help```. ### Citation ``` @misc{dechunwang_2018_1728203, author = {dechunwang}, title = {dechunwang/SSH-pytorch: First release}, month = nov, year = 2018, doi = {10.5281/zenodo.1728202}, url = {https://doi.org/10.5281/zenodo.1728202} } ```

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：