nv-tesseract-ad-diffusion

NV-Tesseract-AD 2.0 Overview Description: NVIDIA NV-Tesseract-AD-2.0 provides anomaly detection functionality. Rather than relying only on transformers, it introduces diffusion modeling, stabilized through curriculum learning, and pairs it with adaptive thresholding methods in a model purpose-built for anomaly detection. Together, these elements address some of the most challenging issues in the field: noisy, high-dimensional signals that drift over time and contain rare, irregular events. This model is for research and development only. License/Terms of Use: Governing Terms: **Model:** The model is provided under the Apache License, Version 2.0. Deployment Geography: Global Use Case: Companies, organizations, research hubs looking to do anomaly detection on temporal data. Release Date: 29th June 2026 via https://huggingface.co/nvidia/nv-tesseract-ad-diffusion Reference(s): Segmented Confidence Sequences and Multi-Scale Adaptive Confidence Segments for Anomaly Detection in Nonstationary Time Series ImDiffusion Model Architecture: Architecture Type:** Diffusion based transformer Network Architecture:** ResNet34 Number of model parameters:** 2 million The NV-Tesseract-AD-2.0 is a diffusion-based model for time-series imputation and anomaly detection. Input: Input Type(s):** Tabular numeric Input Format(s):** Tabular Pandas DataFrame or CSV/JSON Input Parameters:** Two-Dimensional (2D) Other Properties Related to Input:** Pre-Processing Needed Anomaly detection: Contains timestamp column and one or more numeric value columns. Output: Output Type(s):** Tabular numeric Output Format:** Tabular Pandas DataFrame Output Parameters:** Two-Dimensional (2D) Other Properties Related to Output:** Post-Processing Needed Anomaly detection: Contains timestamp, value, and anomaly label columns. Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the mode