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---
pipeline_tag: object-detection
inference: false
datasets:
- Mai0313/coco-pose-2017
tags:
- Pose Estimation
- YOLO-NAS-Pose
- Jetson Orin
- JetPack 5.1.1
- TensorRT 8.5.2
---
We offer a TensorRT model in various precisions including int8, fp16, fp32, and mixed, converted from Deci-AI's [YOLO-NAS-Pose](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md) 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](https://huggingface.co/datasets/cppe-5) dataset to calibrate int8 model~~
More information on calibration for post-training quantization, check [this slide](https://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf)
# 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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