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We offer a TensorRT model in various precisions including int8, fp16, fp32, and mixed, converted from Deci-AI's YOLO-NAS-Pose pre-trained weights (which is only allowed for non-commerical use) in PyTorch. This (TensorRT) model is compatible with JetPack 5.1.1, benchmarked and tested on Jetson Orin Nano Deveoper Kit.

Note that all quantization that has been introduced in the conversion is purely static, meaning that the corresponding model has potentillay bad accuracy compared to the original one.

Todo: use cppe-5 dataset to calibrate int8 model

More information on calibration for post-training quantization, check this slide

Large

Model Name ONNX Precision TensorRT Preicion Throughput (TensorRT)
yolo_nas_pose_l_fp16.onnx.best.engine FP16 FP32+FP16+INT8 46.7231 qps
yolo_nas_pose_l_fp16.onnx.fp16.engine FP16 FP32+FP16 29.6093 qps
yolo_nas_pose_l_fp32.onnx.best.engine FP32 FP32+FP16+INT8 47.4032 qps
yolo_nas_pose_l_fp32.onnx.engine FP32 FP32 15.0654 qps
yolo_nas_pose_l_fp32.onnx.fp16.engine FP32 FP32+FP16 29.0005 qps
yolo_nas_pose_l_fp32.onnx.int8.engine FP32 FP32+INT8 47.9071 qps
yolo_nas_pose_l_int8.onnx.best.engine INT8 FP32+FP16+INT8 36.9695 qps
yolo_nas_pose_l_int8.onnx.int8.engine INT8 FP32+INT8 30.9676 qps

Medium

Model Name ONNX Precision TensorRT Preicion Throughput (TensorRT)
yolo_nas_pose_m_fp16.onnx.best.engine FP16 FP32+FP16+INT8 58.254 qps
yolo_nas_pose_m_fp16.onnx.fp16.engine FP16 FP32+FP16 37.8547 qps
yolo_nas_pose_m_fp32.onnx.best.engine FP32 FP32+FP16+INT8 58.0306 qps
yolo_nas_pose_m_fp32.onnx.engine FP32 FP32 18.9603 qps
yolo_nas_pose_m_fp32.onnx.fp16.engine FP32 FP32+FP16 37.193 qps
yolo_nas_pose_m_fp32.onnx.int8.engine FP32 FP32+INT8 59.9746 qps
yolo_nas_pose_m_int8.onnx.best.engine INT8 FP32+FP16+INT8 44.8046 qps
yolo_nas_pose_m_int8.onnx.int8.engine INT8 FP32+INT8 38.6757 qps

Small

Model Name ONNX Precision TensorRT Preicion Throughput (TensorRT)
yolo_nas_pose_s_fp16.onnx.best.engine FP16 FP32+FP16+INT8 84.7072 qps
yolo_nas_pose_s_fp16.onnx.fp16.engine FP16 FP32+FP16 66.0151 qps
yolo_nas_pose_s_fp32.onnx.best.engine FP32 FP32+FP16+INT8 85.5718 qps
yolo_nas_pose_s_fp32.onnx.engine FP32 FP32 33.5963 qps
yolo_nas_pose_s_fp32.onnx.fp16.engine FP32 FP32+FP16 65.4357 qps
yolo_nas_pose_s_fp32.onnx.int8.engine FP32 FP32+INT8 86.3202 qps
yolo_nas_pose_s_int8.onnx.best.engine INT8 FP32+FP16+INT8 74.2494 qps
yolo_nas_pose_s_int8.onnx.int8.engine INT8 FP32+INT8 63.7546 qps

Nano

Model Name ONNX Precision TensorRT Preicion Throughput (TensorRT)
yolo_nas_pose_n_fp16.onnx.best.engine FP16 FP32+FP16+INT8 91.8287 qps
yolo_nas_pose_n_fp16.onnx.fp16.engine FP16 FP32+FP16 85.4187 qps
yolo_nas_pose_n_fp32.onnx.best.engine FP32 FP32+FP16+INT8 105.519 qps
yolo_nas_pose_n_fp32.onnx.engine FP32 FP32 47.8265 qps
yolo_nas_pose_n_fp32.onnx.fp16.engine FP32 FP32+FP16 82.3834 qps
yolo_nas_pose_n_fp32.onnx.int8.engine FP32 FP32+INT8 88.0719 qps
yolo_nas_pose_n_int8.onnx.best.engine INT8 FP32+FP16+INT8 80.8271 qps
yolo_nas_pose_n_int8.onnx.int8.engine INT8 FP32+INT8 74.2658 qps

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Dataset used to train pesi/YOLO-NAS-Pose-JetPack5