The document discusses improvements made to deep learning based object detection models. It describes issues with the R-CNN model such as slow training and testing speeds. The Fast R-CNN model is then introduced which improves on R-CNN by only extracting features once and using ROI pooling for faster training and testing. Faster R-CNN further improves speed by incorporating a Region Proposal Network to generate proposals instead of using selective search. Finally, SSD is discussed as a single-shot detector that evaluates detections at multiple scales for real-time performance. Throughout, techniques for improving accuracy such as hard negative mining and data augmentation are also covered.