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pytorch based implementation of faster rcnn framework(Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)
This module includes backbone feature extraction network
- vgg16:vgg16 net network(Very Deep Convolutional Networks for Large-Scale Image Recognition)
- fpn101:resnet101 fpn network(Deep Residual Learning for Image Recognition) (Feature Pyramid Networks for Object Detection)
- hrnet:high resolution net(Deep High-Resolution Representation Learning for Visual Recognition)
- mobile_net:mobile_net v2 network(MobileNetV2: Inverted Residuals and Linear Bottlenecks)
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.
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) dataset dataloader IO
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
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