Code repository for YOLO v3 - Robust Deep Learning Object Detection in 1 Hour, published by Packt
This is the code repository for YOLO v3 - Robust Deep Learning Object Detection in 1 Hour, published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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, Robust and Fast Annotation and Data Augmentation, Supervisely handles duplicate images. You can Train your AI Models Online (for free) from anywhere in the world, once you've set up your Deep Learning Cluster. So as you can see, that the features mentioned above can save you a tremendous amount of time. All the code and supporting files for this course are available at: https://github.com/PacktPublishing/YOLO-v3---Robust-Deep-Learning-Object-Detection-in-1-Hour
- Use the Rust trait system to build super flexible types
- Effective ways of handling Rust Errors without Boilerplate code
- How pointer lifetimes keep your code effective and safe
- Work within the restrictions needed for pointer safety, and still achieve capable code
- How rust programs can work with other system programs
- Write programs that take full advantage of multicore processors, using concurrency without the risk of data races
- Loop on anything with the Iterator Trait
- How to Use the Rocket web framework to build powerful websites quickly
To fully benefit from the coverage included in this course, you will need:
This course is for students with python, OpenCV or AI experience who want to learn how to do Object detection with Yolo V3. Those who do not need or already have a theoretical understanding of Object Detection, CNN's and Yolo Architecture. Those who are looking for a practical only approach to Object Detection with Yolo V3.