Hey Andy,
Sorry, about the wait, we do try to answer the forums ASAP but things have been a bit hectic on our end with the holiday season.
As bob also mentioned, we don’t have people actively checking the forum on the weekends as we are only opened on weekdays.
Anyway, to answer your question from what I could find, “Cocoa” (as mentioned in the tutorial) is a group of object-oriented frameworks, created by apple for use on their products.
Though it was originally designed with iOS in mind, it is available to use on most non-apple operating systems including Linux and Microsoft Windows.
The apple documentation for it is available here: What Is Cocoa?
When looking into this I also discovered CocoaPods on Github. They focus on third party open-source Cocoa libraries and their git hub page has an instillation guide and getting started guide linked into it.
I would defiantly try checking them out as they have a lot of cool community made stuff.
Github: GitHub - CocoaPods/CocoaPods: The Cocoa Dependency Manager.
Homepage: https://cocoapods.org/
In addition to this, check out these pre-trained Machine Vision models:
TensorFlow’s Model Zoo: TensorFlow’s detection model zoo provides a variety of pre-trained models for object detection.
Caffe Model Zoo: It’s a popular place to share convolutional models.
PyTorch Hub: An online repository by Pytorch where you can access pre-trained models.
ModelDepot: An open-platform for sharing machine learning models.
ONNX Model Zoo: It provides a collection of pre-trained models ready to be used with ONNX-compatible frameworks.
OpenVINO’s Pretrained Models: Intel’s pre-trained models that are optimized for performance, size, and accuracy.
Remember to comply with their respective licenses when using these pre-trained models.
Lastly, I would take a look at ‘Object and Animal Recognition With Raspberry Pi and OpenCV’ on our website here: Object and Animal Recognition With Raspberry Pi and OpenCV - Tutorial Australia
This is our project guide for object recognition, also made by Tim, and includes links to the coco library.
Hope this helps, good luck with you project!
Cheers,
Sophia 