How to verify classification model trained on classification dataset on a detection dataset for classification purpose? I am working on a problem that involves two tasks - detection and classification. There is no single dataset for both tasks. I am training two models, separate on detection dataset and another on classification dataset. I use the images from the detection dataset as input and get classification predictions on top of detected bounding boxes.
Dataset description :

Classification - Image of the single object (E.g. Car) in the center with a classification label.
Detection - Image with multiple objects (E.g. 4 Cars) with bounding box annotations.

Task - Detect objects(e.g. cars) from detection datasets and classify them into various categories.
How do I verify whether the classification model trained on the classification dataset is working on images from detection dataset? (In terms of classification accuracy)
I cannot manually label the images from the detection dataset for individual class labels. (Need expert domain knowledge)
How do I verify my classification model?
Is there any technique to do this ? Like domain transfer or any weakly-supervised method ?
To verify the accuracy of the classification stage, you will need labeled images with a single car.
To train and verify accuracy of the detection stage and full system, you can:

in the datasets with images with multiple cars, manually, mark the image rectangles that contains one car.
from previous, split the image in one or more ones, each one containing a single car.
pass each one of the previous image with a single car to the classification stage (that means assume classification has 100% accuracy). Record its outputs (labeled cars).
now, from output of steps 1) and 3), you can produce labeled images with multiple cars. Use it to train the detector and verify full system accuracy.