feeding annotations as ground truth along with the images to the model
We need to feed the bounding boxes to the loss function. We need to design a custom loss function, preprocess the bounding boxes and feed it back during back propagation.
I am working on an object detection model. I have annotated images whose values are stored in a data frame with columns (filename,x,y,w,h, class). I have my images inside /drive/mydrive/images/ directory. I have saved the data frame into a CSV file in the same directory. So, now I have annotations in a CSV file and images in the images/ directory. 
I want to feed this CSV file as the ground truth along with the image so that when the bounding boxes are recognized by the model and it learns contents of the bounding box.
How do I feed this CSV file with the images to the model so that I can train my model to detect and later on use the same to predict bounding boxes of similar images?
I have no idea how to proceed.
I do not get an error. I just want to know how to feed the images with bounding boxes so that the network can learn those bounding boxes.