26Īccuracy Best Practices and Inputs/Outputs. Overall Rankings of Opportunities and Challenges. Review of Survey Objectives and Questions. 6Īccuracy Best Practices and Inputs/Outputs. National Cooperative Highway Research Program (NCHRP) Project 10-77Įarthworks Engineering Research Center DirectorĪssociate Professor of Construction Engineering WORKSHOP ON USE OF AUTOMATED MACHINE GUIDANCE (AMG) Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages. MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book.MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
We provide colab tutorial, and full guidance for quick run with existing dataset and with new dataset for beginners. Please see get_started.md for the basic usage of MMDetection. Please refer to get_started.md for installation. Some other methods are also supported in projects using MMDetection. Side-Aware Boundary Localization (ECCV'2020).
Mixed Precision (FP16) Training (ArXiv'2017).Results and models are available in the model zoo. Please refer to changelog.md for details and release history.įor compatibility changes between different versions of MMDetection, please refer to compatibility.md. Add abstract and sketch of the papers in readmes.This project is released under the Apache 2.0 license. The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.Īpart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.
The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet. Faster RCNN, Mask RCNN, RetinaNet, etc.Īll basic bbox and mask operations run on GPUs. The toolbox directly supports popular and contemporary detection frameworks, e.g. Support of multiple frameworks out of box We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. The master branch works with PyTorch 1.5+. MMDetection is an open source object detection toolbox based on PyTorch.