Can I avoid annotating my dataset for object recognition?
No, not really, Deep Learning is not magic, you need bounding box annotations. The field of research that deals with this problem is Weakly Supervised Object Detection, and its a research field, there are no solutions that perform as well as using an annotated dataset.
I've been working on image classification with deep learning models (CNN with keras and tensorflow as back end) such as AlexNet and ResNet. I learned a lot about the whole dataset, learning, testing processes.
I'm now shifting to object detection and have done a lot of research. I came across R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN and different versions of YOLO networks. I noticed that these object detection networks require dataset annotation instead of the former simply needing to have cropped images stored in corresponding files.
Is there any way to accomplish object detection without having to annotate dataset?