I have a problem, im designing a multiclass classifier to classify medic images, I have to classify in which grade of desease is it, this are 6 grades , each time the joint deforms a little, so, mi original dataset was imabalanced, each class have like 16 to 200 images that are very very similar with some little deatils of change, ,so i did some contrasts and flips to the images to have like 500 images per class aprox, i used a vgg16 architechture to train the model but, it soesnt work 30% acc, i ddid a simple model with 2 layers of convolution, I have like 70% of accurracy but my recall is very very low like 0.001 to 0.3, and when I predict with an image, all the images goes to the incorrect class, i dont know how to correct my model , use other metrics or some architechture for this type of images?.Here's a sample of the code https://github.com/Franciscogtu/OARSI-IMAGE-CLASSIFIER.git Thank you for the help
- check that you shuffle the test and train data - make sure that the test and train data have a decent amount of example for each class
- monitor the training and validation loss
- clip your gradient (monitor the gradient value)
- lower the learning rate (or schedule the learning rate)
it's complicated without more information. Can you post the model code?