# NAS_FPN_Tensorflow

**Repository Path**: qf0708443/NAS_FPN_Tensorflow

## Basic Information

- **Project Name**: NAS_FPN_Tensorflow
- **Description**: NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-30
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection 

## Abstract     
This repo is based on [FPN](https://github.com/DetectionTeamUCAS/NAS_FPN_Tensorflow), and completed by [YangXue](https://github.com/yangxue0827).     

## Train on COCO train2017 and test on COCO val2017 (coco minival).     
![1](fpn_voc_2007.gif)

## COCO
|Model|Backbone|Train Schedule|GPU|Image/GPU|FP16|Box AP|
|-----|--------|--------------|---|---------|----|---------------|
|Faster (Face++ & Detectron)|R50v1-FPN|1X|8X TITAN Xp|2|no|36.4|
|Faster (SimpleDet)|R50v1-FPN|1X|8X 1080Ti|2|no|36.5|
|Faster (ours)|R50v1-FPN|1X|1X TITAN Xp|1|no|36.1|
|Faster (ours)|R50v1-FPN|1X|4X TITAN Xp|1|no|36.1|

|Model|Backbone|Pyramid method|Train Schedule|GPU|Image/GPU|Stacks|Dimension|3x3 relu|Box AP|
|-----|--------|----|--------------|---|---------|----|----|----|---------------|
|Faster (ours)|R50v1|FPN|1X|4X TITAN Xp|1|0|256|no|36.1|
|Faster (ours)|R50v1|FPN|1X|8X 2080Ti|1|3|256|yes|35.8|
|Faster (ours)|R50v1|NAS-FPN|1X|8X 2080Ti|1|3|256|yes|37.9|
|Faster (ours)|R50v1|NAS-FPN|1X|8X 2080Ti|1|7|256|yes|38.1|
|Faster (ours)|R50v1|NAS-FPN|1X|8X 2080Ti|1|7|384|yes|38.9|

## My Development Environment
1、python3.5 (anaconda recommend)             
2、cuda9.0 **(If you want to use cuda8, please set CUDA9 = False in the cfgs.py file.)**                    
3、[opencv(cv2)](https://pypi.org/project/opencv-python/)    
4、[tfplot](https://github.com/wookayin/tensorflow-plot) (optional)             
5、tensorflow == 1.12                   

## Download Model
### Pretrain weights
1、Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz), [resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights.       
2、**(Recommend)** Or you can choose to use a better backbone, refer to [gluon2TF](https://github.com/yangJirui/gluon2TF).    
* [Baidu Drive](https://pan.baidu.com/s/1GpqKg0dOaaWmwshvv1qWGg), password: 5ht9.          
* [Google Drive](https://drive.google.com/drive/folders/1BM8ffn1WnsRRb5RcuAcyJAHX8NS2M1Gz?usp=sharing)   

### Trained weights
**Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py, then download the corresponding [weights](https://github.com/DetectionTeamUCAS/Models/tree/master/NAS_FPN_Tensorflow).**      

## Compile
```  
cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace
```

## Train

1、If you want to train your own data, please note:  
```     
(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py     
(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord.py 
```     

2、make tfrecord
```  
cd $PATH_ROOT/data/io/  
python convert_data_to_tfrecord_coco.py --VOC_dir='/PATH/TO/JSON/FILE/' 
                                        --save_name='train' 
                                        --dataset='coco'
```      

3、multi-gpu train
```  
cd $PATH_ROOT/tools
python multi_gpu_train.py
```

## Eval
```  
cd $PATH_ROOT/tools
python eval_coco.py --eval_data='/PATH/TO/IMAGES/'  
                    --eval_gt='/PATH/TO/TEST/ANNOTATION/'
                    --GPU='0'
``` 

## Tensorboard
```  
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
``` 
![3](images.png)
![4](scalars.png)

## Reference
1、https://github.com/endernewton/tf-faster-rcnn   
2、https://github.com/zengarden/light_head_rcnn   
3、https://github.com/tensorflow/models/tree/master/research/object_detection        
4、https://github.com/CharlesShang/FastMaskRCNN       
5、https://github.com/matterport/Mask_RCNN      
6、https://github.com/msracver/Deformable-ConvNets