TensorRTx provides reference implementations of major deep learning models using TensorRT’s native network definition APIs rather than ONNX or other parsers. It exports trained weights to .wts format, loads them into manually constructed TensorRT networks, and builds optimized inference engines. This approach offers flexibility to modify layers, integrate preprocessing/postprocessing into the network, and enables step-by-step debugging. The repo covers detection models (YOLOv3-v13, DETR, RetinaFace), classification networks (ResNet, MobileNet, Swin Transformer), and recognition models (ArcFace, CRNN), all optimized for deployment via TensorRT.