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https://github.com/610265158/faceboxes-tensorflow
a tensorflow implement faceboxes
https://github.com/610265158/faceboxes-tensorflow
faceboxes facedetect facedetector tensorflow tensorflow2
Last synced: about 5 hours ago
JSON representation
a tensorflow implement faceboxes
- Host: GitHub
- URL: https://github.com/610265158/faceboxes-tensorflow
- Owner: 610265158
- License: apache-2.0
- Created: 2019-07-26T16:46:17.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-21T21:32:16.000Z (almost 2 years ago)
- Last Synced: 2024-08-02T19:01:56.757Z (3 months ago)
- Topics: faceboxes, facedetect, facedetector, tensorflow, tensorflow2
- Language: Python
- Homepage:
- Size: 9.08 MB
- Stars: 47
- Watchers: 4
- Forks: 18
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Tensorflow2 - 610265158/faceboxes-tensorflow
README
# [faceboxes](https://arxiv.org/abs/1708.05234)
## introduction
A tensorflow 2.0 implement faceboxes.
**CAUTION: this is the tensorflow2 branch,
if you need to work on tensorflow1,
please switch to tf1 branch**
And some changes has been made in RDCL module,
to achieve a better performance and run faster:
1. input size is 512 (1024 in the paper), then the first conv stride is 2, kernel size 7x7x12.
2. replace the first maxpool by conv 3x3x24 stride 2
3. replace the second 5x5 stride2 conv and maxpool by two 3x3 stride 2 conv
4. anchor based sample is used in data augmentaion.
codes like below
```
with tf.name_scope('RDCL'):
net = conv2d(net_in, 12, [7, 7], stride=2,activation_fn=tf.nn.relu, scope='init_conv1')
net = conv2d(net, 24, [3, 3], stride=2, activation_fn=tf.nn.crelu, scope='init_conv2')
net = conv2d(net, 32, [3, 3], stride=2,activation_fn=tf.nn.relu,scope='conv1x1_before1')
net = conv2d(net, 64, [3, 3], stride=2, activation_fn=tf.nn.crelu, scope='conv1x1_before2')
return net
```
**I want to name it faceboxes++ ,if u don't mind**
Pretrained model can be download from:
+ [baidu disk](https://pan.baidu.com/s/14glOjQYRxKL-QPPHl6HRRQ) (code zn3x )
+ [google drive](https://drive.google.com/open?id=1KO2PuHiBgQEY5uOyLGdFbxBlqPAosY-s)
Evaluation result on fddb
| fddb |
| :------: |
| 0.96 |
**Speed: it runs over 70FPS on cpu (i7-8700K), 30FPS (i5-7200U), 140fps on gpu (2080ti) with fixed input size 512, tf2.0, multi thread.**
**And i think the input size, the time consume and the performance is very appropriate for application :)**
Hope the codes can help you, contact me if u have any question, [email protected] .
## requirment
+ tensorflow2.0
+ tensorpack (data provider)
+ opencv
+ python 3.6
## useage
### train
1. download widerface data from http://shuoyang1213.me/WIDERFACE/
and release the WIDER_train, WIDER_val and wider_face_split into ./WIDER,
2. download fddb, and release FDDB-folds into ./FDDB ， 2002,2003 into ./FDDB/img
3. then run
```python prepare_data.py```it will produce train.txt and val.txt
(if u like train u own data, u should prepare the data like this:
`...../9_Press_Conference_Press_Conference_9_659.jpg| 483(xmin),195(ymin),735(xmax),543(ymax),1(class) ......`
one line for one pic, **caution! class should start from 1, 0 means bg**)
4. then, run:
`python train.py`
and if u want to check the data when training, u could set vis in train_config.py as True
### finetune
1. (if u like train u own data, u should prepare the data like this:
`...../9_Press_Conference_Press_Conference_9_659.jpg| 483(xmin),195(ymin),735(xmax),543(ymax),1(class) ......`
one line for one pic, **caution! class should start from 1, 0 means bg**)
2. set config.MODEL.pretrained_model='./model/detector/variables/variables', in train_config.py,
and the model dir structure is :
```
./model/
├── detector
│ ├── saved_model.pb
│ └── variables
│ ├── variables.data-00000-of-00001
│ └── variables.index
```
3. adjust the lr policy
4. `python train.py`
### evaluation
```
python test/fddb.py [--model [TRAINED_MODEL]] [--data_dir [DATA_DIR]]
[--split_dir [SPLIT_DIR]] [--result [RESULT_DIR]]
--model Path of the saved model,default ./model/detector
--data_dir Path of fddb all images
--split_dir Path of fddb folds
--result Path to save fddb results
```
example `python model_eval/fddb.py --model model/detector
--data_dir 'FDDB/img/'
--split_dir FDDB/FDDB-folds/
--result 'result/' `
### visualization
2. `python vis.py --img_dir your_images_dir --model model/detector `
3. or use a camera:
`python vis.py --cam_id 0 --model model/detector`
You can check the code in vis.py to make it runable, it's simple.
### reference
### [FaceBoxes: A CPU Real-time Face Detector with High Accuracy](https://arxiv.org/abs/1708.05234)