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.gitignore

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+# Byte-compiled / optimized / DLL files
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+*.py[cod]
+*$py.class
+
+# C extensions
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+
+# Distribution / packaging
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+*/tools/demos/*
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+*/data/tfrecord/*

LICENSE

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+MIT License
+
+Copyright (c) 2018 DetectionTeamUCAS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Focal Loss for Dense Object Detection
+
+## Abstract
+This is a tensorflow re-implementation of [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf), and it is completed by [YangXue](https://github.com/yangxue0827).
+
+![1](voc_2007.gif)
+
+### Performance
+| Model |    Backbone    |    Training data    |    Val data    |    mAP    | Train Schedule | GPU | Image/GPU |
+|:------------:|:------------:|:------------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|
+| [Faster-RCNN](https://github.com/DetectionTeamUCAS/Faster-RCNN_Tensorflow)  | ResNet50_v1 600 | VOC07 trainval | VOC07 test | 73.09 | - | 1X GTX 1080Ti | 1 |
+| [FPN](https://github.com/DetectionTeamUCAS/FPN_Tensorflow) | ResNet50_v1 600 | VOC07 trainval | VOC07 test | 74.26 | - | 1X GTX 1080Ti | 1 |
+| RetinaNet | ResNet50_v1 600 | VOC07 trainval | VOC07 test | 73.16 | - | 8X GeForce RTX 2080 Ti | 1 |
+| RetinaNet | ResNet50_v1d 600 | VOC07 trainval | VOC07 test | 73.26 | - | 8X GeForce RTX 2080 Ti | 1 |
+| RetinaNet | ResNet50_v1d 600 | VOC07+12 trainval | VOC07 test | 79.66 | - | 8X GeForce RTX 2080 Ti | 1 |
+| RetinaNet | ResNet50_v1 600 | COCO train2017 | COCO val2017 (coco minival) |  | 1x | 8X GeForce RTX 2080 Ti | 1 |
+
+## My Development Environment
+1、python3.5 (anaconda recommend)             
+2、cuda9.0                     
+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、Or you can choose to use a better backbone, refer to [gluon2TF](https://github.com/yangJirui/gluon2TF). [Pretrain Model Link](https://pan.baidu.com/s/1GpqKg0dOaaWmwshvv1qWGg), password: 5ht9.
+
+### 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/RetinaNet_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 line 76 of $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'
+                    
+``` 
+
+```  
+cd $PATH_ROOT/tools
+python eval_coco_multiprocessing.py --eval_data='/PATH/TO/IMAGES/'  
+                                    --eval_gt='/PATH/TO/TEST/ANNOTATION/'
+                                    --gpu_ids='0,1,2,3,4,5,6,7'           
+``` 
+
+## 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/fizyr/keras-retinanet