for my ML project I want to use the faster_rcnn_resnet101_kitti model from tensorflow model zoo. As the number of images in the Kitti dataset is extremely small (about 7000 images) for a deep learning practice, I was wondering how this small amount of data leads to the decent inference performance (mAP@0.5=87)? One answer I can imagine is that the network was first trained on a different, rich dataset and fine tuned on the Kitti but I am not sure about it. I am wondering how can I find out the exact underlying training procedure (apart from pipeline.config) for the models published on TF model zoo?
Thanks
