Think about the difference between an incorrect suggestion from Google when shopping and a missed blizzard warning. What level of accuracy you consider acceptable can vary considerably by application. You’ll know you have an acceptable model when it performs accurately for your given task. There may not be one right choice for your task. How do you know you’ve chosen the right model? It’s a very simple architecture to understand and may perform adequately depending on the problem. Personally, I see nothing wrong with using AlexNet as a starting point. I would start from the first category and continue to use more complex models as necessary.

Start here: SqueezeNet, MobileNet-v2, ShuffLeNet, NASNetMobile These models are intended to have a low-memory footprint, for embedded devices like Raspberry Pi. When moving to hardware, model size can become increasingly important. Start here: ResNet-50, Inception-v3, Densenet-201, Xception The difference from basic models is that higher-accuracy models may require more training time and have a more complicated network structure. Most networks, including the basic models above, fall into this category. These models cover your image-based workflows, such as image classification, object detection, and semantic segmentation. Start here: GoogLeNet, VGG-16, VGG-19, and AlexNet Once you have a handle on how you want to train your model, you can move to the next section to see if you can improve your results. They tend to have fewer layers, and allow for quick iterations on preprocessing and training options. These are models with simple architectures to get you up and running with confidence. Instead of thinking of all pretrained models as a long list of options, I like to think of them in categories instead. These posts are great at describing the individual models, but why were they chosen? And more importantly: How do you choose a starting point to train a deep learning model? We have talked about pretrained models in blog in previous posts: You can see the latest pretrained models available on the MATLAB Github page where you'll see there are many models to choose from. In this post, I'll walk through the first of 3 questions answered in the column, with a link to more articles at the end. I'm excited to share the latest article with you today: All About Pretrained Models. Recently, I have been writing short Q&A columns on deep learning.

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