The inference time of yolov5s.pt is 0.28s on jetson nano (use python detect.py). Is this normal speed?

[glenn-jocher]

@WyattAutomation see Tutorials for YOLOv5 TensorRT Deployment:

YOLOv5 Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Supervisely Ecosystem  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• TorchScript, ONNX, CoreML Export 🚀
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• TensorRT Deployment

[WyattAutomation]

I will check that out-- thanks!

[PiotrG1996]

Commenting here, since I only get about 9-10FPS via the GPU inference on the Jetson Nano using Yolov5s. Cuda 10.2, Pytorch 1.7.0. Please open the issue until we find a solution to this bottleneck.

Hey, have you found a solution to that bottleneck? In my case, it's also 9-10FPS. I rose an issue here https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading03#issuecomment-1109052469 .

[glenn-jocher]

Hey @PiotrG1996, to improve the FPS on Jetson Nano, you can try the following:

• Use a smaller model like YOLOv5s or YOLOv5m.
• Optimize the trained model using TensorRT.
• Utilize a reduced resolution for inference on edge devices.

Let me know if you need further assistance!