mmdeploy

Why we included this project

If you train or fine-tune models in the OpenMMLab ecosystem and need them running on something other than a desktop GPU, this is the bridge between PyTorch checkpoints and real hardware. It converts OpenMMLab models into several inference formats and runtimes: TensorRT and ONNX Runtime for servers, OpenVINO on CPU, ncnn on mobile and edge, plus CANN, RKNN, and SNPE for specific accelerators, so one model can target very different devices. On top of conversion, it includes a modular C/C++ SDK for building inference applications with pluggable preprocessing, network execution, and post-processing stages. That combination is worth knowing about for teams shipping detection, segmentation, and pose models to production, where the practical question is usually which backend will run fast enough on the target device rather than which framework trained the model.