I have the following problem statement in which I only need to predict whether a given image is an apple or not. For training only 8 images are provided with the following details:
- apple_1 image – 2400×1889 PNG
- apple_2 image – 641×618 PNG
- apple_3 image – 1000×1001 PNG
- apple_4 image – 500×500 PNG contains a sticker on top of fruit
- apple_5 image – 2400×1889 PNG
- apple_6 image – 1000×1000 PNG
- apple_7 image – 253×199 JPG
- apple_8 image – 253×199 JPG
I am thinking about using Transfer learning: either VGG or ResNet-18/34/50. Maybe ResNet is an overkill for this problem statement? How do I deal with such varying image sizes and of different file extensions (PNG, JPG)?
Any online code tutorial will be helpful. I found this example code online.