Training Object Detection model on just 10 images I am trying to train an object detection model using Mask-RCNN with Resnet50 as backbone. I am using the pre-trained models from PyTorch's Torchvision library. I have only 10 images that I can use to train. Of the same 10 images, I am using 3 images for validation. For the evaluation, I am using the evaluation method used in COCO dataset which is also provided as .py scripts in the TorchVision's github repository.
To have enough samples for training, I am oversampling the same 10 images by a factor of 100 i.e. I end up with 1000 images that I can use to train my model. Similarly, I end up having 300 images that I can use for validation.
Now, the problem is that I am getting 0% mAP after train and 0% recall. I have two questions:
Q1. Why would it return 0% mAP?
If it has something to do with the fact that I am oversampling to a large extent, then my next question is
Q2.  Shouldn't the oversampling just cause the model to Overfit and instead provide a higher training as well as validation accuracy for my case (since I have picked the validation data from the training data itself?
Consider using data Augmentation first, you'll have a couple of hundred new images for you to work with, then add some noisy images since to avoid over-fitting. This way you'll have better results.