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https://github.com/tonandr/face_vijnana_yolov3
Face detection keras model based on yolov3.
https://github.com/tonandr/face_vijnana_yolov3
face-detection keras yolov3
Last synced: 23 days ago
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Face detection keras model based on yolov3.
- Host: GitHub
- URL: https://github.com/tonandr/face_vijnana_yolov3
- Owner: tonandr
- License: mit
- Created: 2019-05-17T04:46:41.000Z (over 5 years ago)
- Default Branch: face_recog
- Last Pushed: 2024-05-03T19:47:19.000Z (6 months ago)
- Last Synced: 2024-05-03T21:00:51.713Z (6 months ago)
- Topics: face-detection, keras, yolov3
- Language: Jupyter Notebook
- Homepage:
- Size: 8.51 MB
- Stars: 5
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Face vijnana yolov3
## Face detection Keras model using yolov3 as a base model and a pretrained model, including face detection
Using the pretranied [yolov3 Keras model](https://github.com/experiencor/keras-yolo3), The face detection model is developed using uncontrained college students face dataset provided by [UCCS](https://vast.uccs.edu/Opensetface/) and referring to [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf).
# Tasks
- [x] Develop face vijnana yolov3.
- [x] Train and evaluate face detector with the UCCS dataset.
## Tasks status
This project is closed.
## Test environments
The face detection model has been developed and tested on Linux(Ubuntu 16.04.6 LTS), Anaconda 4.6.11, Python 3.6.8,
Tensorflow 1.13.1 (Keras's backend), Keras 2.2.4 and on 8 CPUs, 52 GB memory, 4 x NVIDIA Tesla K80 GPUs.
## Training and testing procedure
### Install [Anaconda](https://docs.anaconda.com/anaconda/install/linux/)
### After installing Anaconda, create the environment
```conda create -n tf1_p36 python=3.6```
### Go to the created environment
```conda activate tf1_p36```
### Install [CUDA Toolkit 10.1](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=debnetwork)
### Install [cuDNN v7.6.0 for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-download)
### Install tensorflow
```conda install tensorflow==1.13.1```
or
```conda install tensorflow-gpu==1.13.1```
### Download the face detection git repository
```git clone https://github.com/tonandr/face_vijnana_yolov3.git```
### Install face vijnana yolov3
```cd face_vijnana_yolov3```
```python setup.py sdist bdist_wheel```
```pip install -e ./```
```cd src\space```
### Download yolov3 pretrained model weight
```wget https://pjreddie.com/media/files/yolov3.weights```
### Make the resource directory
In the resource directory, make the training and validation folders, and copy training images & training.csv into the training folder and validation images & validation.csv into the validation folder.
The dataset can be obtained from [UCCS](https://vast.uccs.edu/Opensetface/).
### Configuration json format file (face_vijnana_yolov3.json)
```
{
"fd_conf": {
"mode": "train",
"raw_data_path": "/home/ubuntu/face_vijnana_yolov3/resource/training",
"test_path": "/home/ubuntu/face_vijnana_yolov3/resource/validation",
"output_file_path": "solution_fd.csv",
"multi_gpu": false,
"num_gpus": 4,
"yolov3_base_model_load": false,
"hps": {
"lr": 0.0001,
"beta_1": 0.99,
"beta_2": 0.99,
"decay": 0.0,
"epochs": 67,
"step": 1,
"batch_size": 40,
"face_conf_th": 0.5,
"nms_iou_th": 0.5,
"num_cands": 60,
"face_region_ratio_th": 0.8
},
"nn_arch": {
"image_size": 416,
"bb_info_c_size": 6
},
"model_loading": false
},
"fi_conf": {
"mode": "fid_db",
"raw_data_path": "/home/ubuntu/face_vijnana_yolov3/resource/training",
"test_path": "/home/ubuntu/face_vijnana_yolov3/resource/validation",
"output_file_path": "solution_fi.csv",
"multi_gpu": false,
"num_gpus": 4,
"yolov3_base_model_load": false,
"hps": {
"lr": 0.000001,
"beta_1": 0.99,
"beta_2": 0.99,
"decay": 0.0,
"epochs": 35,
"step": 1,
"batch_size": 16,
"sim_th": 0.2
},
"nn_arch": {
"image_size": 416,
"dense1_dim": 64
},
"model_loading": true
}
}
```
### First, train the face detection model
It is assumed that 4 Tesla K80 GPUs are provided. You should set mode to "train". For accelerating computing, you can set multi_gpu to true and the number of gpus.
```python face_detection.py```
You can download [the pretrained face detection Keras model](https://drive.google.com/open?id=1pzGO4YyR46VaMLNeP4_462vWWydAAnYG).
It should be moved into face_vijnana_yolov3/src/space.
### Evaluate the model via generating detection result images, or test the model
Set mode to 'evaluate' or 'test', and you should set model_loading to true.
```python face_detection.py```
### Create subject faces and database
Mode should be set to "data" in fi_conf.
```python face_identification.py```
You can download [subject faces](https://drive.google.com/open?id=1rq_0Fd7Wqmug6c6wfBxzpys-z8yQRgje) and
[the relevant meta file](https://drive.google.com/open?id=1zUhU7v4eB7Fdx-QVXng0ksg_77PIiytW).
The subject face folder should be moved to the resource folder, and the relevant meta file should be moved to
the src/space folder.
### Train the face identification model
Set mode to "train". To train the model with previous weights, you should set model_loading to true.
```python face_identification.py```
### Evaluate the model via generating detection result images, or test the model
Set mode to 'evaluate' or 'test', and you should set model_loading to true.
```python face_identification.py```
# Performance
## Face detection performance
### Calculate mean average precision according to IoU threshold
After getting the face detection solution file of solution_fd.csv, mAP could be calculated as follows.
```python evaluate.py -m cal_map_fd -g validation.csv -s solution_fd.csv```
the result is saved in p_r_curve.h5 as the hdf5 format, so you load it and analyze the performance.
### Current face detection performance
We have evaluated face vijnana yolov3's face detection performance with the UCCS dataset. Yet, the model wasn't trained until saturation, so via training more, the performance can be enhanced.
mAP
AP50
AP55
AP60
AP65
AP70
AP75
AP80
AP85
AP90
AP95
23.57
67.21
58.35
46.61
33.04
19.45
8.41
2.32
0.35
0.0172
0.0000635
There are [face detection result images](https://drive.google.com/open?id=1JelgyzOEN1WNXUl1HKIY2eH_yEcaJ7fA).