I am new at using the jetson TX2 and I really wanted to try out the object detection HOWEVER I am very lost on how to do this. Ive read several papers but i am still VERY lost. does anybody have a step by step tutorial (also listing the programs that i need to download) on how to achieve object detection real time? I was kind of gravitating towards using Single Shot Multibox method but let me know what you guys think its best. Also if anyone knows of any good/useful websites to do this link them below.
If you want to use SSD, you should build their branch by your own.
But if you can use another algorithm, check these pages:
It will guide you to use detectNet in details.
I’ve written a couple of blog posts sharing my experiences running YOLOv2 and Single Shot Multibox Detector (SSD) on Jetson TX2.
I also shared my experiences with NVIDIA DetectNet in earlier posts. Feel free to check them out.
I shared a python script which could do real-time SSD object detection with either JTX2 on-board camera, USB webcam or IP CAM. Please refer to the following blog post.
Hi @jkjung13 .
Thank you so much for your posts, they are very helpful.
I have some questions about object detection with Jetson TX2 that I need to be clarified.
I believe that you use caffe and tensorflow in your code. Have you tried pytorch ? It can get the same speed as tensorflow or caffe ?
Does coding in python affect the performance ? is C++ better ?
I am quite new in this problem so I am very appreciated if someone can help me on this.
Thank you so much in advance.
A while ago I wrote a series of blog posts about how to train a tensorflow object detector with custom data and how to optimize the model with TF-TRT (TensorRT), and then to deploy it onto Jetson TX2. Please refer to the following forum thread for more details.
As you have pointed out, I have tested caffe and tensorflow. But I don’t have experience using pytorch on JTX2 yet, so I’m not able to comment on that.
Regarding python vs C++, I’d say if your application is mainly feeding images to DNN for inference (utilizing the GPU) and without much other pre- or post-processing, then the difference in performance should be negligible. In my opinion, programming in python is preferred because of development speed and ease of maintenance.
Thank you for your response. I will read more in detail your blog posts.
I don’t have experience on TensorFlow so it would be harder for me than with Pytorch but I will try.