Fine Tuning YOLOv7 on Custom Dataset

In this blog post, we will be fine tuning the YOLOv7 object detection model on a real-world pothole detection dataset. Since its inception, the YOLO family of object detection models have come a long way. YOLOv7 is the most recent addition to this famous anchor-based single-shot family of object detectors. It comes with a bunch of improvements which includes state-of-the-art accuracy and speed. Benchmarked on the COCO dataset, the YOLOv7 tiny model achieves more than 35% mAP and the YOLOv7 (normal) model achieves more than 51% mAP. It is also equally important that we get good results when fine tuning such a state-of-the-art model. For that reason, we will be fine tuning YOLOv7 on a real-world pothole detection dataset in this blog post.