JETSON TX2 上的 YoloV3 部署

Question

I faced problem regarding Yolo object detection deployment on TX2. I use pre-trained Yolo3 (trained on Coco dataset) to detect some limited objects (I mostly concern on five classes, not all the classes), The speed is low for real-time detection, and the accuracy is not perfect (but acceptable) on my laptop. I'm thinking to make it faster by multithreading or multiprocessing on my laptop, is it possible for yolo? But my main problem is that algorithm is not running on raspberry pi and nvidia TX2.

Here are my questions:

1. In general, is it possible to run yolov3 on TX2 without any modification like accelerators and model compression techniques?

2. I cannot run the model on TX2. Firstly I got error regarding camera, so I decided to run the model on a video, this time I got the 'cannot allocate memory in static TLS block' error, what is the reason of getting this error? the model is too big. It uses 16 GB GPU memory on my laptop.The GPU memory of raspberry and TX2 are less than 8GB. As far as I know there are two solutions, using a smaller model or using tensor RT or pruning. Do you have any idea if there is any other way?

3. if I use tiny-yolo I will get lower accuracy and this is not what I want. Is there any way to run any object detection model with high performance for real-time in terms of both accuracy and speed (FPS) on raspberry pi or NVIDIA TX2?

4. If I clean the coco data for just the objects I concern and then train the same model, I would get higher accuracy and speed but the size would not change, Am I correct?

5. In general, what is the best model in terms of accuracy for real-time detection and what is the best in terms of speed?

6. How is Mobilenet? Is it better than YOLOs in terms of both accuracy and speed?

Answer 1

1- Yes it is possible. I already run Yolov3 on Jetson Nano.

2- It depends on model and input resolution of data. You can decrease input resolution. Input images are transferred to GPU VRAM to use on model. Big input sizes can allocate much memory. As far as I remember I have run normal Yolov3 on Jetson Nano(which is worse than tx2) 2 years ago. Also, you can use Yolov3-tiny and Tensorrt as you mention. There are many sources on the web like this & this .

3- I suggest you to have a look at here . In this repo, you can make transfer learning with your dataset & optimize the model with TensorRT & run it on Jetson.

4- Size not dependent to dataset. It depend the model architecture(because it contains weights). Speed probably does not change. Accuracy depends on your dataset. It can be better or worser. If any class on COCO is similiar to your dataset's any class, I suggest you to transfer learning.

5- You have to find right model with small size, enough accuracy, gracefully speed. There is not best model. There is best model for your case which depend on also your dataset. You can compare some of the model's accuracy and fps here .

6- Most people uses mobilenet as feature extractor. Read this paper. You will see Yolov3 have better accuracy, SSD with MobileNet backbone have better FPS. I suggest you to use jetson-inference repo.

By using jetson-inference repo, I get enough accuracy on SSD model & get 30 FPS. Also, I suggest you to use MIPI-CSI camera on Jetson. It is faster than USB cameras.

Answer 2

I fixed the problem 1 and 2 only by replacing import order of the opencv and tensorflow inside the script.Now I can run Yolov3 without any modification on tx2. I got average FPS of 3.