Further modifications on this repo will be updated on Gitlab temporarily for about a month (before 9.15).

1. Choose Faster RCNN or Mask RCNN (rewrite and run)
2. Understand RoIAlign & RoIPooling
3. Read two papers:

Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition

Perceptual Generative Adversarial Networks for Small Object Detection

1. Three DL programs to quickly warm up and get familiar with PyTorch; more importantly, to gain experience on how to adjust parameters and architecture.
2. Test1: A simple FCN implementation on PASCAL-VOC-2007.
3. Test2: A classification network using CNN & ResNet on CIFAR-10.
4. Test3: A classfication & localization network on CUB-200-2011.

1. Do multiple tests on PASCAL-VOC-2007 with Faster-RCNN from faster-rcnn.pytorch.
2. Records all the runtime output.

1. Do tests based on the same source code.
2. Modify the pretreatment and evaluation parts of the code.
3. Records all the runtime output.

1. Comment on the Faster-RCNN source code.

1. Read and comment on the code of RoIAlign & RoIPooling.

1. note1.txt: The progress of object detection, starts from RCNN, through SPP and Fast-RCNN, ends with Faster-RCNN.
2. note2.txt: About RPN, etc.
3. note3.txt: Notes on Tutorial provided by faster-rcnn.pytorch.
4. note4.txt: Details on RoI-Pooling, RoI-Align and RoI-Warping.
5. note5.txt: Study PASCAL-VOC-2007 & TT100K datasets.