Helping with elevating YOLOv11's performance in Human Detection task

[BurhanQ]

@np4s

• Suppose I want to train YOLOv11 to detect a brand new class, would including COCO dataset is still needed?

Yes. This is probably the most common question I’ve heard. Consider the information about classes “lost” from any prior training.

@np4s

• For the additional data, would the person-only dataset I have collected is enough to overcome the problem of falsely detecting canines, or should I include images of dogs, cats, etc., too? And if yes, what will be the ideal ratio among the classes? If most images in the dataset don’t have multiple classes (eg: an image of dogs only), will it still contribute to the training?

There is no “ideal ratio” only what works for your application. You will have to collect and label data until the model performs as well as you need it to. Remember that no model is perfect, and there will still be some likelihood of misclassification. Including animals will be helpful, as you provide examples to the model on what a person is not.

@np4s

• I intended to train on my custom dataset only (which is not included data from sources like roboflow). This dataset includes ~4000 images, 8000 instances of person class, and the images are cropped from 50 CCTV videos at 1fps rate. However, the videos are not in 640x640 px, will this affect the training result? Moreover, albeit having lots of images, a lot of them share the same background setting (surroundings, lighting, ect.) and many images have same person instances; the videos are different camera angles. What would you think of this dataset?

I think having much more variety in your image dataset can help. It’s likely the lack of variety in data leading to the model to overfit and stop training earlier than it should. Supplementing your dataset with additional images will be a big help. It would be good to remove any duplicate or near-duplicate images from your dataset, as they won’t add much value, only training time.

@np4s

• I first used COCO YOLOv11 to label all the images and then manually modify the labels. I omit all person label that are too close/far to the camera, have missing body parts or have “abnormal” postures (is sitting, bending, etc.). Is this a good labelling practice?

They’re still a person, so they should still be labeled as such. If you can tell it’s a person that label should be there. Otherwise it will ‘confuse’ the model, as it has detected a something that resembles a person, but the information you provided says it is not.

@np4s

• Can you share any detailed, or reliable sources I can read about preparing dataset, labeling, and evaluating the “clealiness” of the dataset? I am very new to this training on custom dataset, and preparing my own data, so kinda at a loss here

fiftyone FiftyOne — FiftyOne 1.3.0 documentation is a great tool for evaluating datasets and find errors. There are other libraries you can find with a simple web search, but I would recommend fiftyone.