One single model for all classes

Hi, I want to train a single model for all classes (cars/pedestrians/cyclists or maybe trucks and vans). I changed the config file but when I run the code it says it cannot find some files: FileNotFoundError: /home/isaac/python/avod/avod/data/mini_batches/iou_2d/kitti/train/lidar/All[ 0.5 0.5 0.5]/000580.npy not found for sample 000580 in /home/isaac/python/avod/avod/data/mini_batches/iou_2d/kitti/train/lidar, run the preprocessing script first

I check the folder and found some folders such as cars[0.5], people[0.5, 0.5], but no all[0.5, 0.5, 0.5]. So I have two question:

• Did you train a single network for all classes and the performance was bad and just don’t want to release it? Cuz you mentioned different configs for your different networks for cars and people. I think because of different sizes cause these different configs.
• If I want to train a single network, which files do I need to modify.

I think training one single network with a single config and pre-defined 3d anchors can cause lower accuracy and higher inference time. Correct me if I’m wrong, please.

Do you suggest to do this or not.

Thank you.

[Fredrik00]

You need to create a preprocessing config, such as those in mb_preprocessing that includes the classes Car Pedestrian and Cyclist. After that you need to set up preprocessing in gen_mini_batches.py to process All using the config you set up. If you want more classes you will at least also have to modify kitti_dataset.py as it is hardcoded to name joint detection of Pedestrian and Cyclist as ‘People’ and Pedestrian, Cyclist and Car as ‘All’, but I haven’t tested expanding this yet.

I have forked the repo and done some work of my own, you can see examples of the two first files I mentioned at https://github.com/Fredrik00/avod/blob/master/avod/configs/mb_preprocessing/test_all.config and https://github.com/Fredrik00/avod/blob/master/scripts/preprocessing/gen_mini_batches.py

Keep in mind that results on pedestrians and cyclists will not be very good out of the box, likely due to the poor balancing of the classes in the dataset.

[Jiachenyin1]

亲~邮件已经收到啦，我会及时查看的