pesi/YOLO-NAS-Pose-JetPack5

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