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Adam solvers are the hassle free standard for optimizers. Empirically, Adam solvers converge faster and are more robust towards hyper-parameter …

Adam can be understood as updating weights inversely proportional to the scaled L2 norm (squared) of past gradients. AdaMax extends this to the …

AdamW is very similar to Adam . It only differs in the way how the weight decay is implemented. The way how it's implemented in Adam came from the …

In advanced options we currently only have one option available to users. The "Automated tools generate..." toggle allows you to decide if our …

AI assistants

AI assistants are what we call our AI tooling that you can use to automate parts - or all - of your annotation work. The concept behind them is …

AI assistants status overview

Out of the many questions we get from our users, many concerns the status and training of our AI assistant models. We’re the first to admit this has …

While the Adam optimizer, which made use of momentum as well as the RMS prop, was efficient in adjusting the learning rates and finding the optimal …

ASGD

Average Stochastic Gradient Descent, abbreviated as ASGD, averages the weights that are calculated in every iteration.  w_{t+1}=w_t-\eta \nabla …

Attribute Prediction

Attribute Prediction dashboard Widgets GPU Consumption Running time Inference time Hamming Score VS Number of Iterations Loss VS Number of Iterations …

Automated labelling

Automated labelling is a way for you to batch-process images in your project. Essentially, you take a model used in your project (Object Detection …

Average Loss

Average loss is the average of various losses that is arise in a model. Average loss varies from model to model since different types of loss arise …

Base Learning Rate

The learning rate defines how large the steps of your optimizer are on your loss landscape. The base learning rate defines at which learning rate …