[PyTorch] Official implementation of CVPR2022 paper "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers". https://arxiv.org/abs/2203.11496
Really interesting and exciting work.
I was wondering about the number of detections TransFusion predicts. When visualizing my training results I always get full 200 detections (num_proposals). In the visualizations of your paper that does not seem to be the case. Did you modify the score_threshhold in the bbox_coder or am I missing something else?
In #48 you also provided the evaluation results for the training without the fade strategy, and it looks like there are also around 155 predictions per sample (your results).
If that is the case, do you know why is this not a problem for the mAP metric?
Really interesting and exciting work. I was wondering about the number of detections TransFusion predicts. When visualizing my training results I always get full 200 detections (num_proposals). In the visualizations of your paper that does not seem to be the case. Did you modify the score_threshhold in the bbox_coder or am I missing something else? In #48 you also provided the evaluation results for the training without the fade strategy, and it looks like there are also around 155 predictions per sample (your results). If that is the case, do you know why is this not a problem for the mAP metric?