Developing real-world applications
Recognizing cats and dogs is a cool problem but less likely a problem of importance. Real-world applications of image classification used in products may be different. You may have different data, targets, and so on. In this section, you will learn the tips and tricks to tackle such different settings. The factors that should be considered when approaching a new problem are as follows:
- The number of targets. Is it a 10 class problem or 10,000 class problem?
- How vast is the intra-class variance? For example, does the different type of cats have to be identified under one class label?
- How vast is the inter-class variance? For example, do the different cats have to be identified?
- How big is the data?
- How balanced is the data?
- Is there already a model that is trained with a lot of images?
- What is the requisite for deployment inference time and model size? Is it 50 milliseconds on an iPhone or 10 milliseconds on Google Cloud Platform? How much RAM can be consumed...
