How to Train model in which number of classes is equal to number of instances in particular image used for prediction
A little improvement can be achieved through data augmentation. For every image (in your case, every student), you could say that an object rotated by 10 percent, is still the same object for the observer. The same goes for mirroring the image. By adding a couple of new data points this way, you would probably get better results.
Suppose one classroom contains 40 students.
Then I trained model for all images of 40 students and then try to detect all 40 students in one picture.
But not getting good accuracy.
Why this is happing? Is any other measure criteria other than just measuring the accuracy of the model?