How to improve recall of faster rcnn object detection model

Question

I'm retraining a faster rcnn inception coco model for detecting brand of products on shelf.

I stopped the model around 400k steps when total loss dropped under 0.1 over a period of time. The recall was around 65% and precision was 40% with 95% confidence cut-off threshold.

Learning rate started at 0.00001 and configured to reduce to 0.000005 after 200k steps.

The dataset size is 15 classes with at least 100 annotated boxes for each class. Total number of images is 300.

How to improve recall of the model?

Should I change to faster rcnn ras (which has higher mAP but I don't think precision is as important as recall in my use case)?

Another question is: usually what's the recall for a object detection model? Is it very challenging to reach higher than 90%?

Many thanks in advance!

Answer 1

As you are asking about the Faster RCNN model, you can track two different metrics.

1. Precision and Recall for the Region Proposal Network (RPN).

2. Precision and Recall for the RCNN Final output.

The above two metrics can give us a better understanding of how the model is performing.

Case 1: When the recall of RPN is high and low for the RCNN output, then it is clear that, you don't have enough positive labels for the classification network to learn.

Case 2: When the recall of RPN is low and high for the RCNN output, then you might not have enough amount of training data and less number of classes.

Case 3: When both recalls are low, then try larger dataset as your model is already converging.

-- Experimenting with learning rate always helps.

-- Simple Hack : You can use multiple aspect ratios (near to your original aspect ratios) so that you can get more labels for training (Not sure how well it helps in your case).

Answer 2

You could try using image augmentation to expand your training dataset. 300 images is not much. Try looking at https://github.com/aleju/imgaug.