This issue is about the evaluating the detection of a model. I do not completely understand the full code right now, but basically the code is right there and we may want to look into the code at train/kitti_eval/evaluate_object_3d_offline.cpp.
The followings are some of the excerpts from here.
Summary
There are 2 types of evaluation: detection and localization
In 3D detection evaluation, we use 3D bounding box overlap to determine true/false positives/negatives.
In 3D localization evaluation, the bounding boxes (that our model predicts) are first projected to the bird’s eye view plane. Then IoU is evaluated on oriented 2D boxes.
There are 3 level of difficulties defined, or performance metrics:
This issue is about the evaluating the detection of a model. I do not completely understand the full code right now, but basically the code is right there and we may want to look into the code at
train/kitti_eval/evaluate_object_3d_offline.cpp
.The followings are some of the excerpts from here.
Summary
EASY
MODERATE
HARD
Results
Pre-trained model
Trained model (epoch ~50)