In this post I share how to use python code (with OpenCV) to capture and display camera video on Jetson TX2, including IP CAM, USB webcam and the Jetson onboard camera. This sample code should work on Jetson TX1 as well.
I trained a DetectNet model with the data I prepared from Kaggle's 'The Nature Conservancy Fisheries Monitoring' dataset. Here's the list of steps I employed for the training, as well as a brief discussion about the result.
I took fish image data from Kaggle's 'The Nature Conservancy Fisheries Monitoring' competition, and wanted to train a 'fish detector' with it. I chose NVIDIA DetectNet as the underlying object detector. In this post I documented how I prepared fish image training data for DetectNet.
I ran the Deep Leanring Cats Dogs Tutorial code to train an AlexNet on Jetson TX2. This is the best Caffe and Pyhton tutorial I've come across so far. I highly recommend it to people who wants to learn Caffe.
I finally managed to put everything together, and started training my AI Agent (DeepMind's DQN) to play 'Galaga' on Nintendo Famicom Mini. Hopefully the AI could learn in a couple of weeks to play significantly better than the random player so that I could report the progress then...