I am training pre-trained SSD-InceptionV2-Coco to detect the "car",
which is one of the classes in mscoco label.
I train the model with ~50k sample from KITTI, 500k iteration with batch size 2.
I followed this script to generate tfrecord file.
Then I test both original pre-trained model and my trained model with one video.
The performance of my trained model is worse. More missing detected results.
One thing I found recently is the classification_loss/localization_loss increases when AvgNumGroundtruthBoxesPerImage increases.
Another thing I found is the more ground truth boxes per image I have,
the less average num positive anchors per image I have.
This bothers me because if the number of anchors generated per image is fixed,
more ground truth boxes should provide more positive anchors per image.
So I wonder where to find the root cause.
Any suggestion is welcome.
Thank you for precious time on my question.