In this article, we will guide you through the process of implementing the YOLO-NAS-Pose model using TensorRT on the Jetson Orin Nano Developer Kit. This setup can help you achieve high-performance pose estimation tasks with various model options and precisions.

What You Need

• Jetson Orin Nano Developer Kit
• JetPack 5.1.1
• TensorRT 8.5.2
• Pre-trained YOLO-NAS-Pose weights (available for non-commercial use)
• A setup with PyTorch

Understanding the Model Conversion

To illustrate the conversion of the YOLO-NAS-Pose model to TensorRT, imagine you are an artist who has created a beautiful painting. Now, you want to replicate this painting in various sizes (representing different precisions). Each size retains the essence of your original work while allowing you to showcase it in different settings (just like how FP16, FP32, and INT8 serve different purposes in model deployment). However, when the painting is resized (quantized), some of the fine details may be lost, leading to a compromise in accuracy—much like how static quantization might affect model performance, as stated in our notes.

Steps to Implement YOLO-NAS-Pose

1. Install Required Packages

Ensure you have all necessary packages installed for PyTorch and TensorRT. You can do this via your terminal:

sudo apt-get install python3-pyTorch python3-tensorrt

2. Acquire YOLO-NAS-Pose Weights

Download the YOLO-NAS-Pose pre-trained weights from this GitHub repository. Make sure to comply with the non-commercial use policy.

3. Convert the Model to TensorRT

Once you have the weights, you can convert them to TensorRT with various precision options. Here’s how:

python convert_model.py --weights_path yolonas_pose_weights.pth --output_path yolonas_pose.trt

4. Load the Model for Use

After conversion, load the model in your application to start inference:

import tensorrt as trt

def load_model(trt_file):
    with open(trt_file, 'rb') as f:
        engine = trt.Runtime(trt.Logger(trt.Logger.WARNING)).deserialize_cuda_engine(f.read())
    return engine

model = load_model("yolonas_pose.trt")

5. Run Inference

Now that your model is ready, you can run pose estimation on input images using this function:

def run_inference(engine, image):
    # Add your inference code here
    pass

Troubleshooting Tips

If you encounter issues during installation or model conversion, consider the following troubleshooting ideas:

• Ensure that all dependencies are correctly installed and fully updated.
• Check if the TensorRT and JetPack versions are compatible with your setup.
• For any model conversion errors, revisit your conversion script and paths to ensure they point to valid files.
• Monitor memory usage on the Jetson device to avoid out-of-memory errors.

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Performance Benchmarks

Here are some throughput benchmarks for the models converted using TensorRT:

# Model Benchmarks
# Large Model
# Precision: FP16
# Throughput: 46.7231 qps
# Medium Model
# Precision: FP32
# Throughput: 58.0306 qps
# Small Model
# Precision: INT8
# Throughput: 74.2494 qps

Conclusion

By following the steps outlined above, you can successfully implement the YOLO-NAS-Pose model on Jetson Orin using TensorRT. This powerful combination allows for efficient pose estimation while leveraging the strengths of the Jetson platform.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.