How to remove undetected bounding boxes

[drNoob13]

Hi Team,

Thanks to your instruction, I am able to run your code to train, evaluate and inference on the Kitti dataset.

After running the demo generation for 2d image demos/show_predictions_2d.py, I see lots of green bounding boxes as image below. Would you mind letting me know the color coding invention you are using? What is the difference between yello and red?

And, importantly, how could I disable those green boxes?

In addition, I am wondering if you have a script to generate the demo for 3d point cloud also. Any hint would be greatly appreciated.

Thank you,

[drNoob13]

It could be a mismatch between the config file I used in train/eval/inference and the one used in the demo script. I have changed the config file to the one I used in the above process. The new bounding boxes look more reasonable but still there is issue with the wrong detected car on the bottom right of the image. Also having segmentation fault in the demo_2d running script, which didn't happen before.

I am checking new code and doing all steps again to see if the problem persists.

/avod $  python3 demos/show_predictions_2d.py 
Available steps: ['0', '1000', '2000', '3000', '4000', '5000', '6000', '7000', '8000', '9000', '10000', '11000', '12000', '13000', '14000', '15000', '16000', '17000', '18000', '19000', '20000', '21000', '22000', '23000', '24000', '25000', '26000', '27000', '28000', '29000', '30000', '31000', '32000', '33000', '34000', '35000', '36000', '37000', '38000', '39000', '40000', '41000', '42000', '43000', '44000', '45000', '46000', '47000', '48000', '49000', '50000', '51000', '52000', '53000', '54000', '55000', '56000', '57000', '58000', '59000', '60000', '61000', '62000', '63000', '64000', '65000', '66000', '67000', '68000', '69000', '70000', '71000', '72000', '73000', '74000', '75000', '76000', '77000', '78000', '79000', '80000', '81000', '82000', '83000', '84000', '85000', '86000', '87000', '88000', '89000', '90000', '91000', '92000', '93000', '94000', '95000', '96000', '97000', '98000', '99000', '100000', '101000', '102000', '103000', '104000', '105000', '106000', '107000', '108000', '109000', '110000', '111000', '112000', '113000', '114000', '115000', '116000', '117000', '118000', '119000', '120000']
Prediction images saved to: /avod/avod/data/outputs/pyramid_cars_with_aug_example/predictions/images_2d/predictions/val/120000/0.1
Saving 1654 / 3769, Avg Time: 0.589s, Time Remaining: 1246.94sSegmentation fault

[drNoob13]

My issue is solved by running training/evaluation/inference of only one configuration for each git clone.