YOLOv3 – Robust Deep Learning Object Detection in 1 hour

Learn how we implemented YOLO V3 Deep Learning Object Detection Models From Training to Inference – Step–by–Step

When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is that if you are just getting started learning about AI Object Detection,  you may encounter some of the following common obstacles along the way:

Labeling dataset is quite tedious and cumbersome,

Annotation formats between various object detection models are quite different.

Labels may get corrupt with free annotation tools,

Unclear instructions on how to train models – causes a lot of wasted time during trial and error.

Duplicate images are a headache to manage.

This got us searching for a better way to manage the object detection workflow, that will not only help us better manage the object detection process but will also improve our time to market.

Amongst the possible solutions we arrived at using Supervisely which is free Object Detection Workflow Tool, that can help you:

Use AI to annotate your dataset,

Annotation for one dataset can be used for other models (No need for any conversion) – Yolo, SSD, FR–CNN, Inception etc,