tao▌

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DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with

Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the data-services container (`tao_toolkit.data_services` from `versions.yaml`) directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.

Deformable DETR for 2D object detection. Uses deformable attention for efficient multi-scale feature processing,

Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts

CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF

BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view

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Brev managed GPU instances with Docker support. Use when running TAO training, evaluation, or inference on

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