Fast R-CNN is a fast object detection framework using deep convolutional neural networks. It trains state-of-the-art detection models like VGG16 significantly faster than prior methods (R-CNN, SPPnet) and achieves higher mean Average Precision on PASCAL VOC. Built on Caffe with Python bindings, it was described in the ICCV 2015 paper by Ross Girshick.