Hi, I’m working on an object detection pipeline on a Jetson board. I successfully converted the ONNX model and integrated it into DeepStream pipeline using nvinfer.
To avoid replicating the full video processing pipeline for evaluation, which includes camera input, encoding/decoding, inference etc., I’d like to directly evaluate the .engine models in Python.
Specifically, I want to:
- Load the
.enginemodel. - Run inference on a set of images after preprocessing.
- Save the inference results to disk.
This would allow me then to compute some metrics, compare different converted models with varying precisions and analyse their accuracy trade-offs. I haven’t been able to find a complete example that demonstrates such workflow, so I’m looking for help.
• Hardware Platform (Jetson / GPU): Jetson
• DeepStream Version: 1.2.0
• TensorRT Version: 10.3.0
• NVIDIA GPU Driver Version (valid for GPU only): CUDA 12.6