In the wiki, we dive into visionAI concepts of a wide variety of topics. The list will be ever-growing, but below you find our attempt of putting it all into a meaningful structure.
Use the menu on the left-hand side to navigate through the individual terms and categories. The menu on the right-hand to scroll through the different sections of an article.
In this section, we cover the most used model families in visionAI
today. Each model family solves one specific type of problem in computer
vision. We will describe the respective task for each model, give a
brief overview of how these models work, and link other helpful
information like which metrics should be used or the most groundbreaking
papers in this model family.
And yeah, we know that transformers are a thing, but they not viable
for production yet. This is why we're not including them for now.
Most of the fancy papers published are about model architectures. Of course, the range of model architectures out there is endless, so we'll focus on the most commonly used ones and the ones available in Hasty's model zoo. Whenever we add a model there, we'll also add it to the wiki.
If there's an architecture you're passionate about but not present in the wiki yet, please go ahead and add it.
Is it SOTA? Metrics are used to measure the performance of a model.
Each model family and use case requires different metrics. We discuss
which metric you should use for which use case, provide you benchmarks,
an intuition behind each metric, and small code snippets to calculate
Loss is the number we always want to see going down, and ideally,
converging to zero. The loss function is the target function which a
neural network minimizes. It is typically some disparity between a
model's prediction and the ground truth data. Different loss functions
are suitable for different use cases. In the wiki, we'll explore which
loss functions you should use depending on your task and how to compute
Solvers, also called optimizers, are the algorithms that navigate you
through the loss landscape and converge to the minimal loss of your
model. We'll cover the most commonly used ones and dive deeper into the
hyper-parameters which you can set here and how they influence your
The algorithms controlling the optimizer ramp up. Concretely, they
can modulate the learning rate of the optimizer during training.