This is part 3 of the series where we are looking at ways to adapt image augmentation techniques to object detection tasks. In this part, we will cover how to implement how to rotate and shear images as well as bounding boxes using OpenCV's affine transformation features.
Previously, we have covered a variety of image augmentation techniques such as Flipping, rotation, shearing, scaling and translating. This part is about how to bring it all together and bake it into the input pipeline for your deep network.
Learn more about what we've been working on to make Gradient better than ever.
Tutorial on building YOLO v3 detector from scratch detailing how to create the network architecture from a configuration file, load the weights and designing input/output pipelines.
Part 2 of the tutorial series on how to implement your own YOLO v3 object detector from scratch in PyTorch.
Part 3 of the tutorial series on how to implement a YOLO v3 object detector from scratch in PyTorch.
Part 4 of the tutorial series on how to implement a YOLO v3 object detector from scratch using PyTorch.
Part 5 of the tutorial series on how to implement a YOLO v3 object detector from scratch using PyTorch.
