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AlphaJia authored Oct 25, 2020
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pytorch based implementation of faster rcnn framework([Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497))

### Framework Structure
####backbone:
#### backbone:
This module includes backbone feature extraction network
* vgg16:vgg16 net network([Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556))
* fpn101:resnet101 fpn network([Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)) ([Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144))
* mobile_net:mobile_net v2 network([MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381))
####config
#### config
This module includes config parameters in training period and testing period
* test_config: specify config parameters in testing period like model_file, image_path_dir, save_dir, etc.
* train_config: specify config parameters in training period like backbone network, batch_size, image_path_dir, anchor_size, ect.
####dataloader
#### dataloader
This module inherits pytorch dataloader classes, dataset IO.You can also generate your own dataset dataloader IO and put it in this module
* coco_dataset: coco([Common Objects in Context](https://cocodataset.org/#home)) dataset dataloader IO
####test
#### test
This module includes the utils function test(common called unit test, also called UT)
* anchor_utils_test: some unit testing for utils/anchor_utils.py
####utils
#### utils
This module includes some utilies for image processing, network architectures building, anchor generating, loss function, etc.
* anchor_utils: some basic function for building anchors
* im_utils: some basic function for image processing

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