We'll cover state-of-the-art algorithms for image classification, object detection, object instance segmentation, and human pose prediction that we recently developed at Facebook AI Research. Our image classification results are based on the recently developed "ResNeXt" model that supersedes ResNet's accuracy on ImageNet, but much more importantly yields better features with stronger generalization performance on object detection tasks. Using ResNeXt as a backbone, we'll present a unified approach for detailed object instance recognition tasks, such as instance segmentation and human pose estimation. This model builds on our prior work on the Faster R-CNN system with Feature Pyramid Networks, which enables efficient multiscale recognition. We'll describe our platform for object detection research that enables a fast and flexible research cycle. Our platform is implemented on Caffe2 and can train many of these state-of-the-art models on the COCO dataset in 1-2 days using sync SGD over eight GPUs on a single Big Sur server.