Increase FPS on Yolo4 model

Robotics & Edge Computing Jetson & Embedded Systems Jetson AGX Xavier
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Dear all,

Greetings ! This is my first project on Jetson Xavier development kit – JetPack 4.6 L4T 32.6.1. (installed 512 GB NVMeM.2 SSD). I am running Yolo 4 with Darknet on custom trained model. (Wall crack images). The model detects wall crack from live Tello drone video. Yolo4 custom model weights is 256 MB
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During the inference Xavier running on Power Mode 30W - 6 core i am getting only 10 FPS. because of this low FPS there is some issue on detection. When run on 15W - Desktop mode, gives 7 to 8 FPS.** . To get reasonable accuracy at least 18 to 20 FPS or above needed. When i show this demo to my boss, he ask why such a low FPS and wonder the model running on Xavier CPU or GPU. (he has high hope for Jetson Xavier)

though my script executed without any specific instruction to run on - -gpus 0
as explained in this blog -https://jkjung-avt.github.io/yolov4/

Here is data flow → Tello Drone Flying and send video to → Jetson Xavier - that video is feed into the → Yolo4 model during the inference (instead web came the video comes from external source). All run in single script (darknet_video.py ) with 4 threads

python3 darknet_video.py --weightspath yolov4-custom_final.weights --configpath yolov4-custom.cfg --datapath /home/jetson/darknet/data/CrackDetection/obj.data

Running this same model on Windows Laptop - i7-10750H CPU @ 2.60GHz RTX 2060 (6GB) with i am getting 16 to 18 FPS.

So my question ,

  1. Is there any specific way to check /ensure the model runs on GPU itself on Jetson Xavier ?
  2. Is there any basic optimization rules to rule of thumb (Power Mode?) while running to heavy models like YOLO V4 ?
  3. or am i missing some basic points? Any suggestion is welcome.

Cheers!
Chandra

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Hi,

1. The simplest way is to check GPU loading with tegrastats tool.
If the inference is well optimized, it’s expected to see the GPU utilization reach 99%. (GR3D_FREQ 99%@1377)

$ sudo tegrastats

2. It is recommended to use our TensorRT library for inference.
You can also use Deepstream to get a better-optimized pipeline on Jetson
Below is a sample for YOLOv4 for your reference:

Thanks.

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Deep stream is interesting, As beginner and project schedule is short, so i may not time to try this time. On the link the, i saw to TWO methods to run the TensorRT sample of YOLOv4.
Is the YOLOv4 integrating with DeepStream 5.0 more straight forward and beginner friendly?

update: I have tried the YOLOV4-Tiny model-which I getting the 30-40 FPS and predication accuracy reasonable. ‘’
Thanks.

Cheers!
Chandra

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