CODEWITHSUNDEEP
pythonobjectobject-detectionyolov5
Jithin P James
Jithin P James

Reputation: 762

Optimum number of target class objects for yolov5 custom model training

I am trying to train a custom object detector, is ther a limit on the number of target class objects that the yolov5 architecture can be trained on. For example- coco dataset has 80 target class, suppose I have 500 object types to detect, is it advisable to use yolov5.

Can this be explain with reasons.

Upvotes: 0

Views: 1326

Answers (1)

Dinis Rodrigues
Dinis Rodrigues

Reputation: 605

You can add as many classes you want to any network. The yolo architecture is known for giving more attention to inference time rather than to performance. Although achieving good results on conventional datasets, the yolo model is built for speed.

But essentially you want a network that has a good backbone (deep and wide) that can really obtain rich features from your image.

From my experience, there is really no straight forward answer. It depends on your dataset as well, if you have large/medium/small objects to detect. I really recommend trying out different models, because every single model, will perform differently on custom datasets. From here you select the best one. State-of-the-art models don't directly relate to the best model on transfer learning and fine-tuning.

The Yolo and all other single shot detectors, for me, were the ones who worked best for fine-tuning purposes (RetinaNet was best for my use cases so far), they are good for hyper parameter tuning because you can train them fast and test what works and what doesn't. With two stage detectors (Faster-RCNN etc) I never achieved overall good results, mainly because the training process is different and much slower.

I recommend you read this article, it explains both architecture types, pros and cons.

Additionally if you want to train a model for more than 500 classes, Tensorflow Object Detection API has pre-trained models for the OpenImages dataset (600 classes), and there is the Detectron 2 on LVIS dataset (1200 classes). I recommend starting with models which were trained on a higher number of classes if you want to fine tune to a similar number of classes in your dataset.

Upvotes: 2

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