pesi/YOLO-NAS-Pose-JetPack5
By pesi · ↓ 0 · ♥ 5 · Updated 2024-02-27
What's in this repo
This page lists the files in pesi/YOLO-NAS-Pose-JetPack5, including yolo_nas_pose_l_fp16.onnx, yolo_nas_pose_l_fp32.onnx, yolo_nas_pose_l_int8.onnx and more.
- 12 ONNX models (.onnx files)
- 30 files, including 12 .onnx, 12 .txt, 2 .md, 1 .png, 1 .sh, 1 .py
- Last updated 2024-02-27
ONNX files
| File | Size | Download |
|---|---|---|
| yolo_nas_pose_l_fp16.onnx | — | Download |
| yolo_nas_pose_l_fp32.onnx | — | Download |
| yolo_nas_pose_l_int8.onnx | — | Download |
| yolo_nas_pose_m_fp16.onnx | — | Download |
| yolo_nas_pose_m_fp32.onnx | — | Download |
| yolo_nas_pose_m_int8.onnx | — | Download |
| yolo_nas_pose_n_fp16.onnx | — | Download |
| yolo_nas_pose_n_fp32.onnx | — | Download |
| yolo_nas_pose_n_int8.onnx | — | Download |
| yolo_nas_pose_s_fp16.onnx | — | Download |
Showing 10 of 12 .onnx files — see the full file list on the right.
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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 |

Model info
Created: 2023-12-29
Updated: 2024-02-27
View on Hugging Face