Peking University/Baidu - Autonomous Driving

This is the code for my part of 7th place solution in Peking University/Baidu - Autonomous Driving.
Our entire solution is in this thread

I started with hocop1's really great kernel, so basic part of my code is based on his. My main contributions are below:

• Change the CenterNet implementation to this one
• Change backbone to resnet152
• change the depth representation to 1 / (sigmoid(z) - 1
• remove x, y, yaw(actually pitch), roll prediction(we can predict yaw and roll but it was almost same as train set's median)
• remove optimize xyz, just convert rcz to xyz
• increase image size to (832, 2080) or (960, 2400) or (1088, 2720)
• change the distance threshold to 4
• change the confidence threshold to 0.1
• ensemble 5 folds
• remove false positive by using masks in 'test_masks/'

I tried 3 scale ensemble, but it might be not so different from just 5 fold ensemble of image_size=(1088,2720).
My single fold model achieve public0.097/private0.091 and 5 fold ensemble got public0.117/private0.105.
With ensmebling Phalanx's model and Jhui's model, we got public0.129/private0.119.

Usage

$ python resize_images.py --config config/003_r152d_img1088.yml
$ python split_folds.py --config config/003_r152d_img1088.yml
$ python train.py --config config/cls/003_r152d_img1088.yml
$ python prediction.py --config_dir config/003_r152d_img1088.yml --ensemble

Reference

• My code for this competition specific part is based on this great starter kernel
• CenterNet implementation is from see--'s code for Kuzushiji competition
• To evaluate models, this tito's kernel helps me a lot. But eventually we evaluate our model by f1-like score. Please check metric code for the detail.