Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Tensorflow implementation of FPN detection framework.
| Date | Stars |
|---|---|
| 2026-07-24 | 413 |
| 2026-07-25 | 413 |
| 2026-07-28 | 413 |
| 2026-07-30 | 413 |
| 2026-08-06 | 413 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Feature Pyramid Networks for Object Detection
## Recommend an improved version of FPN： https://github.com/DetectionTeamUCAS
A Tensorflow implementation of FPN detection framework.
You can refer to the paper [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144)
Rotation detection method baesd on FPN reference [R2CNN](https://github.com/yangxue0827/R2CNN_FPN_Tensorflow), [RRPN](https://github.com/yangJirui/RRPN_FPN_Tensorflow) and [R2CNN_HEAD](https://github.com/yangxue0827/R2CNN_HEAD_FPN_Tensorflow) and [R-DFPN](https://github.com/yangxue0827/R-DFPN_FPN_Tensorflow)
If useful to you, please star to support my work. Thanks.
## Configuration Environment
ubuntu(Encoding problems may occur on windows) + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080
You can also use docker environment, command: docker pull yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0
## Installation
Clone the repository
```Shell
git clone https://github.com/yangxue0827/FPN_Tensorflow.git
```
## Make tfrecord
The data is VOC format, reference [here](sample.xml)
data path format ($FPN_ROOT/data/io/divide_data.py)
```
├── VOCdevkit
│ ├── VOCdevkit_train
│ ├── Annotation
│ ├── JPEGImages
│ ├── VOCdevkit_test
│ ├── Annotation
│ ├── JPEGImages
```
```Shell
cd $FPN_ROOT/data/io/
python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship'
```
## Demo
1、Unzip the weight $FPN_ROOT/output/res101_trained_weights/*.rar
2、put images in $FPN_ROOT/tools/inference_image
3、Configure parameters in $FPN_ROOT/libs/configs/cfgs.py and modify the project's root directory
4、image slice
```Shell
cd $FPN_ROOT/tools
python inference.py
```
5、big image
```Shell
cd $FPN_ROOT/tools
python demo.py --src_folder=.\demo_src --des_folder=.\demo_des
```
## Train
1、Modify $FPN_ROOT/libs/lable_name_dict/***_dict.py, corresponding to the number of categories in the configuration file
2、download pretrain weight([resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) or [resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)) from [here](https://github.com/yangxue0827/models/tree/master/slim), then extract to folder $FPN_ROOT/data/pretrained_weights
3、
```Shell
cd $FPN_ROOT/tools
python train.py
```
## Test tfrecord
```Shell
cd $FPN_ROOT/tools
python $FPN_ROOT/tools/test.py
```
## eval(Not recommended, Please refer [here](https://github.com/DetectionTeamUCAS/FPN_Tensorflow))
```Shell
cd $FPN_ROOT/tools
python ship_eval.py
```
## Summary
```Shell
tensorboard --logdir=$FPN_ROOT/output/res101_summary/
```
## Graph
## Test results
### airplane
### sar_ship
### ship
## Note
This code works better when detecting single targets, but not suitable for multi-target detection tasks. Recommend improved code: https://github.com/DetectionTeamUCAS/FPN_Tensorflow.
Excerpt of 3,655 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:65cfd6af630b736f, topic:tensorflow