Results for “multilabel-classification”

Computes the LogAUC metric using the torchmetrics implementation for binary, multiclass, or multilabel classification tasks.

Trains, evaluates, and runs inference for Mask Auto-Label (MAL) weakly-supervised segmentation models using ViT-MAE backbones with minimal point or box annotations.

Train, evaluate, distill, quantize, export, and run inference for PyTorch-based TAO image classification models with support for multiple backbones.

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Computes the Recall metric using torchmetrics, including configuration for binary, multiclass, and multilabel tasks.

Compute the Equal Error Rate (EER) metric using torchmetrics for binary, multiclass, or multilabel classification tasks, with reference signatures and usage examples.