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Update DeepStream Doc with YOLO11 and DeepStream 7.1 (#18443)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Lakshantha Dissanayake 2024-12-30 06:40:00 -08:00 • committed by GitHub
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docs/en/guides/deepstream-nvidia-jetson.md
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@ -23,7 +23,8 @@ This comprehensive guide provides a detailed walkthrough for deploying Ultralyti
!!! note !!! note
This guide has been tested with both [Seeed Studio reComputer J4012](https://www.seeedstudio.com/reComputer-J4012-p-5586.html) which is based on NVIDIA Jetson Orin NX 16GB running JetPack release of [JP5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and [Seeed Studio reComputer J1020 v2](https://www.seeedstudio.com/reComputer-J1020-v2-p-5498.html) which is based on NVIDIA Jetson Nano 4GB running JetPack release of [JP4.6.4](https://developer.nvidia.com/jetpack-sdk-464). It is expected to work across all the NVIDIA Jetson hardware lineup including latest and legacy. This guide has been tested with [NVIDIA Jetson Orin Nano Super Developer Kit](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit) running the latest stable JetPack release of [JP6.1](https://developer.nvidia.com/embedded/jetpack-sdk-61),
[Seeed Studio reComputer J4012](https://www.seeedstudio.com/reComputer-J4012-p-5586.html) which is based on NVIDIA Jetson Orin NX 16GB running JetPack release of [JP5.1.3](https://developer.nvidia.com/embedded/jetpack-sdk-513) and [Seeed Studio reComputer J1020 v2](https://www.seeedstudio.com/reComputer-J1020-v2-p-5498.html) which is based on NVIDIA Jetson Nano 4GB running JetPack release of [JP4.6.4](https://developer.nvidia.com/jetpack-sdk-464). It is expected to work across all the NVIDIA Jetson hardware lineup including latest and legacy.
## What is NVIDIA DeepStream? ## What is NVIDIA DeepStream?
@ -38,6 +39,7 @@ Before you start to follow this guide:
- For JetPack 4.6.4, install [DeepStream 6.0.1](https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_Quickstart.html) - For JetPack 4.6.4, install [DeepStream 6.0.1](https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_Quickstart.html)
- For JetPack 5.1.3, install [DeepStream 6.3](https://docs.nvidia.com/metropolis/deepstream/6.3/dev-guide/text/DS_Quickstart.html) - For JetPack 5.1.3, install [DeepStream 6.3](https://docs.nvidia.com/metropolis/deepstream/6.3/dev-guide/text/DS_Quickstart.html)
- For JetPack 6.1, install [DeepStream 7.1](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Installation.html)
!!! tip !!! tip
@ -47,34 +49,48 @@ Before you start to follow this guide:
Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) GitHub repository which includes NVIDIA DeepStream SDK support for YOLO models. We appreciate the efforts of marcoslucianops for his contributions! Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) GitHub repository which includes NVIDIA DeepStream SDK support for YOLO models. We appreciate the efforts of marcoslucianops for his contributions!
1. Install dependencies 1. Install Ultralytics with necessary dependencies
```bash ```bash
pip install cmake cd ~
pip install onnxsim pip install -U pip
git clone https://github.com/ultralytics/ultralytics
cd ultralytics
pip install -e ".[export]" onnxslim
``` ```
2. Clone the following repository 2. Clone the DeepStream-Yolo repository
```bash ```bash
cd ~
git clone https://github.com/marcoslucianops/DeepStream-Yolo git clone https://github.com/marcoslucianops/DeepStream-Yolo
cd DeepStream-Yolo
``` ```
3. Download Ultralytics YOLO11 detection model (.pt) of your choice from [YOLO11 releases](https://github.com/ultralytics/assets/releases). Here we use [yolov8s.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt). 3. Copy the `export_yoloV8.py` file from `DeepStream-Yolo/utils` directory to the `ultralytics` folder
```bash ```bash
wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt cp ~/DeepStream-Yolo/utils/export_yoloV8.py ~/ultralytics
cd ultralytics
```
!!! note
`export_yoloV8.py` works for both YOLOv8 and YOLO11 models.
4. Download Ultralytics YOLO11 detection model (.pt) of your choice from [YOLO11 releases](https://github.com/ultralytics/assets/releases). Here we use [yolo11s.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt).
```bash
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt
``` ```
!!! note !!! note
You can also use a [custom trained YOLO11 model](https://docs.ultralytics.com/modes/train/). You can also use a [custom trained YOLO11 model](https://docs.ultralytics.com/modes/train/).
4. Convert model to ONNX 5. Convert model to ONNX
```bash ```bash
python3 utils/export_yoloV8.py -w yolov8s.pt python3 export_yoloV8.py -w yolo11s.pt
``` ```
!!! note "Pass the below arguments to the above command" !!! note "Pass the below arguments to the above command"
@ -120,7 +136,14 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
--batch 4 --batch 4
``` ```
5. Set the CUDA version according to the JetPack version installed 6. Copy the generated `.onnx` model file and `labels.txt` file to the `DeepStream-Yolo` folder
```bash
cp yolo11s.pt.onnx labels.txt ~/DeepStream-Yolo
cd ~/DeepStream-Yolo
```
7. Set the CUDA version according to the JetPack version installed
For JetPack 4.6.4: For JetPack 4.6.4:
@ -134,24 +157,30 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
export CUDA_VER=11.4 export CUDA_VER=11.4
``` ```
6. Compile the library For Jetpack 6.1:
```bash
export CUDA_VER=12.6
```
8. Compile the library
```bash ```bash
make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo make -C nvdsinfer_custom_impl_Yolo clean && make -C nvdsinfer_custom_impl_Yolo
``` ```
7. Edit the `config_infer_primary_yoloV8.txt` file according to your model (for YOLOv8s with 80 classes) 9. Edit the `config_infer_primary_yoloV8.txt` file according to your model (for YOLO11s with 80 classes)
```bash ```bash
[property] [property]
... ...
onnx-file=yolov8s.onnx onnx-file=yolo11s.pt.onnx
... ...
num-detected-classes=80 num-detected-classes=80
... ...
``` ```
8. Edit the `deepstream_app_config` file 10. Edit the `deepstream_app_config` file
```bash ```bash
... ...
@ -160,7 +189,7 @@ Here we are using [marcoslucianops/DeepStream-Yolo](https://github.com/marcosluc
config-file=config_infer_primary_yoloV8.txt config-file=config_infer_primary_yoloV8.txt
``` ```
9. You can also change the video source in `deepstream_app_config` file. Here a default video file is loaded 11. You can also change the video source in `deepstream_app_config` file. Here a default video file is loaded
```bash ```bash
... ...
@ -183,12 +212,16 @@ deepstream-app -c deepstream_app_config.txt
!!! tip !!! tip
If you want to convert the model to FP16 [precision](https://www.ultralytics.com/glossary/precision), simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yoloV8.txt` If you want to convert the model to FP16 precision, simply set `model-engine-file=model_b1_gpu0_fp16.engine` and `network-mode=2` inside `config_infer_primary_yoloV8.txt`
## INT8 Calibration ## INT8 Calibration
If you want to use INT8 precision for inference, you need to follow the steps below If you want to use INT8 precision for inference, you need to follow the steps below
!!! note
Currently INT8 does not work with TensorRT 10.x. This section of the guide has been tested with TensorRT 8.x which is expected to work.
1. Set `OPENCV` environment variable 1. Set `OPENCV` environment variable
```bash ```bash
@ -303,13 +336,13 @@ deepstream-app -c deepstream_app_config.txt
## Benchmark Results ## Benchmark Results
The following table summarizes how YOLOv8s models perform at different TensorRT precision levels with an input size of 640x640 on NVIDIA Jetson Orin NX 16GB. The following table summarizes how YOLO11s models perform at different TensorRT precision levels with an input size of 640x640 on NVIDIA Jetson Orin NX 16GB.
| Model Name | Precision | Inference Time (ms/im) | FPS | | Model Name | Precision | Inference Time (ms/im) | FPS |
| ---------- | --------- | ---------------------- | --- | | ---------- | --------- | ---------------------- | ---- |
| YOLOv8s | FP32 | 15.63 | 64 | | YOLO11s | FP32 | 14.6 | 68.5 |
| | FP16 | 7.94 | 126 | | | FP16 | 7.94 | 126 |
| | INT8 | 5.53 | 181 | | | INT8 | 5.95 | 168 |
### Acknowledgements ### Acknowledgements
@ -336,17 +369,17 @@ To convert a YOLO11 model to ONNX format for deployment with DeepStream, use the
Here's an example command: Here's an example command:
```bash ```bash
python3 utils/export_yoloV8.py -w yolov8s.pt --opset 12 --simplify python3 utils/export_yoloV8.py -w yolo11s.pt --opset 12 --simplify
``` ```
For more details on model conversion, check out our [model export section](../modes/export.md). For more details on model conversion, check out our [model export section](../modes/export.md).
### What are the performance benchmarks for YOLO on NVIDIA Jetson Orin NX? ### What are the performance benchmarks for YOLO on NVIDIA Jetson Orin NX?
The performance of YOLO11 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLOv8s models achieve: The performance of YOLO11 models on NVIDIA Jetson Orin NX 16GB varies based on TensorRT precision levels. For example, YOLO11s models achieve:
- **FP32 Precision**: 15.63 ms/im, 64 FPS - **FP32 Precision**: 14.6 ms/im, 68.5 FPS
- **FP16 Precision**: 7.94 ms/im, 126 FPS - **FP16 Precision**: 7.94 ms/im, 126 FPS
- **INT8 Precision**: 5.53 ms/im, 181 FPS - **INT8 Precision**: 5.95 ms/im, 168 FPS
These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLO11 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section. These benchmarks underscore the efficiency and capability of using TensorRT-optimized YOLO11 models on NVIDIA Jetson hardware. For further details, see our [Benchmark Results](#benchmark-results) section.