<p>Unfortunately, I do not know where the breaking point is, and of course, it will depend on acceptable evaluation metrics and training data size.</p>
<p>From a technical point of view, there is no hard limit and if you go to extremes there could be Core ML model size issues and memory issues during inferences. However, that will only happen for an extremely large number of classes.</p>
<p>From a modeling perspective (which is a problem that will happen much earlier than the technical limitation) it is not as clear. As you increase the number of classes, you increase the risk of making classification mistakes. Although, the severity of a lot of the mistakes should simultaneously go down as you will have more and more classes that are naturally similar (breeds of dogs, etc.). The original YOLO9000 paper (<a href="https://arxiv.org/pdf/1612.08242.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1612.08242.pdf</a>) trained a model using 9000+ classes with reasonable results (lots of mistakes of course, but still impressive). They trained it on a combination of detection and classification data, so if they actually had detection data for all 9000, then results would presumably be even better.</p>
<p>In your experiment, it sounds like 50-60 was OK (thanks for giving us a sample point!). Anything below 100 is definitely tried and true, as long as you have the data. However, will 300 do OK? Will 1000 do OK? Theoretically, I would say yes, if you are able to provide enough training data and you adjust your expectation of what a good evaluation metric is since you know you'll make more mistakes. For instance, for classification with 1000 classes, it is common to report top-5 accuracy (that is, the correct label is in your top-5 classes for a sample).</p>
<p>Here is a useful link - <a href="https://github.com/apple/turicreate/issues/968" rel="nofollow noreferrer">https://github.com/apple/turicreate/issues/968</a></p>
General object recognition with biggest number of classes
<p>I'm new to the computer vision world, I'm trying to create a script with the objective to gather data from a dataset of images.</p>
<p>I'm interested in what kind of objects are in those images and getting a summary of them in a json file for every image.</p>
<p>I've checked out some YOLO implementations but the ones I've seen are almost always based on COCO and have 80 classes or have a custom dataset.</p>
<p>I've seen that there are algorithms like InceptionV3 etc. which are capable of classifying 1000 classes. But per my understanding object classification is different from object recognition.</p>
<p>Is there a way to use those big dataset classification algos for object detection?
Or any other suggestion?</p>