100 landmarks
Chatoyant19 opened this issue · 4 comments
Hello, thank you for sharing your code. How did you get the 100 landmarks? First get some proposals using BING, and then generate the final detection using a detection? But the results of the final detection are some bounding boxes. I think landmarks should be some points rather than bounding boxes. Thank you very much.
Typically in SLAM, yes, landmarks are points, but in this paper that we were reproducing with CALC, landmarks are bounding boxes of buildings and other prevalent objects. I would suggest reading the linked paper (cited in our paper as well) to get a better picture of what is going on.
Thanks for your response.I train the net from the scratch on the Places dataset(256*256), and I test the model on the campus dataset(your paper).I set the maximum number of iterations to 500k and use iteration 220k(calc_iter_220000.caffemodel) to test the testing dataset as you said in other issues, I also used other iterations to get the caffemodel to test the testing dataset. But my results are much worse than yours. I use the default batch size 256, and a single GPU(GeForce GTX 1080) for training.Can you tell me how to get experimental results similar to yours?
Please see previous issues that discuss this issue. I used a larger batch size with two GPUs. The default is set to 256 for convenience for people who may not have the 12GB GPUs.
@Chatoyant19
Hello~ Have you trained the net successfully? I'm also interested in retraining the net and I think I'm facing the same problem.