• Hardware x86 CPU, NVIDIA GPU machine
• Network Type (Yolo_v3)
• TLT Version (3.22.05)
I have a highly imbalanced object detection dataset, I want to train a yolov3 model on this dataset. I had gone through the documentation YOLOv3 - NVIDIA Docs but I couldn’t find a way to specify weights per class here,
Is it something that can be added in the training config or are we forced to balance the dataset for better results, or is there something else I can do like using a different backbone which can capture more features, currently I am using a resnet18 model as the backbone.
Kindly provide suggestions for getting the best accuracy while working with this imbalanced dataset, I am also open to try out other object detection models if it will give me better results.