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Accuracy

The accuracy is the overall percentage of predictions without errors. It's derived from the confusion matrix . Accuracy used for multi-label …

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 …

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 …

Attribute Prediction

Sample inference script for torchscript exported image-tagger. The following sample code should be run from the export directory: import torch import …

Attributor

Attributors are used in combination with other models such as object detectors , semantic and instance segmentors . They add a layer of meta-data to …

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 …