No_Description_Cascade-RCNN_Tensorflow.zip

# Cascade-RCNN_Tensorflow ## Abstract This is a tensorflow re-implementation of [Cascade R-CNN Delving into High Quality Object Detection ](https://arxiv.org/abs/1712.00726). This project is completed by [YangXue](https://github.com/yangxue0827) and [WangYashan](https://github.com/toubasi). ## Train on VOC 2007 trainval and test on VOC 2007 test (PS. This project also support coco training.)  ## Comparison ### use_voc2012_metric | Stage | AP50 | AP60 | AP70 | AP75 | AP80 | AP85 | AP90 | AP95 | |------------|:---:|:--:|:--:|:--:|:---:|:--:|:--:|:--:| |baseline|75.80|67.25|52.15|41.41|27.98|12.63|2.73|0.11| |1+2+3|75.80|**68.74**|**57.09**|**48.68**|37.70|22.52|7.51|0.54| |1+2|**75.98**|68.40|56.01|46.89|35.67|20.42|6.44|0.39| |1|74.89|65.98|52.45|40.63|27.79|13.22|2.94|0.11| |2|75.67|68.69|56.73|47.82|35.5|20.29|6.46|0.38| |3|74.35|67.62|56.64|48.65|**38.02**|**23.19**|**8.05**|**0.54**| ### use_voc2007_metric | Stage | AP50 | AP60 | AP70 | AP75 | AP80 | AP85 | AP90 | AP95 | |------------|:---:|:--:|:--:|:--:|:---:|:--:|:--:|:--:| |baseline|73.62|65.28|51.93|42.52|29.48|16.2|5.84|1.32| |1+2+3|73.69|66.59|56.19|48.82|39.47|25.57|12.09|2.5| |1+2|**74.01**|66.5|55.53|46.53|36.96|23.6|11.33|2.15| |1|72.92|64.29|52.41|48.8|30.36|16|5.64|2.15| |2|73.55|**66.75**|55.78|48.35|37.39|23.61|10.66|**2.69**| |3|71.58|65.73|**56.64**|**49.08**|**39.68**|**26.25**|**12.28**|2.32| ## Requirements 1、tensorflow >= 1.2 2、cuda8.0 3、python2.7 (anaconda2 recommend) 4、[opencv(cv2)](https://pypi.org/project/opencv-python/) ## Download Model 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 $PATH_ROOT/data/pretrained_weights. 2、please download [mobilenet_v2](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) pre-trained model on Imagenet, put it to $PATH_ROOT/data/pretrained_weights/mobilenet. 3、please download [trained model](https://github.com/DetectionTeamUCAS/Models/tree/master/Cascade_R-CNN_Tensorflow) by this project, put it to $PATH_ROOT/output/trained_weights. ## Data Format ``` ├── VOCdevkit │   ├── VOCdevkit_train │   ├── Annotation │   ├── JPEGImages │ ├── VOCdevkit_test │   ├── Annotation │   ├── JPEGImages ``` ## Compile ``` cd $PATH_ROOT/libs/box_utils/cython_utils python setup.py build_ext --inplace ``` ## Demo **Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py, then download the corresponding [weights](https://github.com/DetectionTeamUCAS/Models/tree/master/Cascade_R-CNN_Tensorflow).** ``` cd $PATH_ROOT/tools python inference.py --data_dir='/PATH/TO/IMAGES/' --save_dir='/PATH/TO/SAVE/RESULTS/' --GPU='0' ``` ## Eval ``` cd $PATH_ROOT/tools python eval.py --eval_imgs='/PATH/TO/IMAGES/' --annotation_dir='/PATH/TO/TEST/ANNOTATION/' --GPU='0' ``` ## 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.py --VOC_dir='/PATH/TO/VOCdevkit/VOCdevkit_train/' --xml_dir='Annotation' --image_dir='JPEGImages' --save_name='train' --img_format='.jpg' --dataset='pascal' ``` 3、train ``` cd $PATH_ROOT/tools python train.py ``` ## Tensorboard ``` cd $PATH_ROOT/output/summary tensorboard --logdir=. ```   ## 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