This post writes the training code of YOLOv3 and carries out some test training sessions on COCO 2014 dataset.
In this post we create a Dataset and a DataLoader class to load the COCO 2014 detection data.
In this post we create 3 essential tools in the object detection task: IoU (Intersection-over-Union), NMS (Non-Maximum suppression) and mAP (mean Average Precision).
In this post we load pre-trained weights for the YOLOv3 model and run some test inferences.
This post talks about reading and parsing the YOLOv3 config file and building a Darknet-53 model using PyTorch.
This is the start of a series on understanding and implementing the YOLOv3 model using PyTorch.