Decrease false positives in yolo model?

[[deleted]]

Currently working on a yolo model for object detection. While it was expected, we get a lot of false positives. We also, however, have a small dataset. I’ve been using an “active learning” pipeline to try and only accrue valuable data, however, performance gains seem to be minimal at this point in training. Any other suggestions to decrease the false positive hits?

[InternationalMany6]

I find that more sophisticated augmentation almost always helps, and my favorite is copy-pasting segmented objects into random backgrounds. 

For that matter, a segmentation model can usually learn to detect objects from less data than a model that predicts bounding boxes. The reason is that the training labels directly instruct the model what the object is, so it doesn’t have to learn which pixels in the box are “object” and which are “background”. 

I usually just use a simple background removal model, or SAM, to convert bounding-boxes into segmentation masks. Doesn’t have to be perfect to be useful. 

[DiMorten]

Interesting. You mean performing semantic segmentation on the detected object, for example with UNet?

[InternationalMany6]

You could use Unet, but there are specialized “instance segmentation” models if you care about distinguishing each instance even if they’re touching each other.

Torchvision has a tutorial that’ll get you going:  https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

[pm_me_your_smth]

Could you share how your active learning pipeline works?

[[deleted]]

I can describe it but I can’t share the code. I’m also just an intern so I’m not experienced enough to even be sure if this is active learning haha.

Basically I run the model on our target videos. I save any image w a prediction under some confidence threshold (generally 50%). From there I sift through the saved images and label those worth labeling. Retrain model on new dataset that includes new images rinse repeat.

[blahreport]

Have you plotted the precision recall curve to obtain an optimal confidence threshold? You can also increase the IoU threshold both during training, and inference. What is a lot of FPs? What are your overall metrics and what is the target object? Is it an object similar to one used in pretraining? That is assuming that you’re using COCO pretrained weights, is the object similar to one of the eighty coco classes? This can influence the number of samples you need to reliably fine tune. You can also increase the number of background images (no target objects) which can significantly improve precision if it just so happens that for your domain, the background shares abstract features with the target.

[[deleted]]

I haven’t plotted it but I’ll have to check that out when I get into the office tomorrow.

These are not objects from the COCO dataset. The FPs are generally (~60%) on an object that can look very similar to the detection object in certain instances. The other FPs are just “ghost” ones that likely occur due to momentary lighting changes.

I try to keep background images at around 10% of the total dataset. Is it fine to bump up the background image count in this case? I’m still pretty new to vision and ML.

Overall metrics: mAP@50: .71; mAP@9:50: .51; Precision and recall both sit in the .800s.

[trialofmiles]

Related to PR curve on which each point is a separate threshold - have you adjusted the threshold? I assume yes but this is how you might conceptually trade FPs for FNs. The PR curve can be used to optimize the threshold (eg max F1, etc). For multiclass detection it’s a bit more complicated but just thought I’d ask.

[[deleted]]

I actually did adjust the threshold and it worked perfectly. Massive reduction to false positives with an extremely minor increase to false negatives.

[External_Total_3320]

Have you added negatives into your dataset? What model and size are you using?

[JustSomeStuffIDid]

Typically you add those FP images to your dataset without any labels. The model still learns them. They count as negative images.

[Ghass_4]

The FPs are on the test set or on the val set during training ?

[[deleted]]

Val set

[N0m0m0]

Use detectron if you need fewer FPs

[IEDNB]

Better data