This work is created by Zhe Xu from UTS. Credit to Ning Zhang, Jeff Donahue, Ross Girshick and Trevor Darrell from UC Berkeley who share the original RCNN and Part-based RCNN code.
- Caffe
- Download caffe from http://caffe.berkeleyvision.org/ and follow the instructions to install.
- Change caffe matlab wrapper path in init.m
- RCNN
- A modified RCNN code is attached in the code patch.
- If you want to access the original RCNN code to see the modifications, download source code from https://github.com/rbgirshick/rcnn and follow the instructions to install.
- Follow rcnn instructions to train the part detectors.
- Liblinear
- Download liblinear package from http://www.csie.ntu.edu.tw/~cjlin/liblinear/
- Download MALSAR package from http://www.public.asu.edu/~jye02/Software/MALSAR/
Annotation/ has annotated part boxes on CUB200-2011 dataset.
- Recommended file: https://drive.google.com/file/d/0Bwo0SFiZwl3JR1RFdnM0R3EyNDA/view
- Full file: https://drive.google.com/file/d/0Bwo0SFiZwl3JSmZNXzZJVG5FbEE/view
- If you have installed cached files, run run_prcnn_sample.m to get results
- To run the whole procedure of extracting selective search data -> finetune part cnns -> train RCNN part detectors -> get augmented training data from weak dataset -> re-finetune part cnns -> get final classification results, go to ./rcnn directory and run rcnn_run.m.
- Modify the paths in config_prcnn.m and get_bird_data.m
###Bug report
If you have any issues running the codes, please contact Zhe Xu ([email protected]).