tao▌

Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for

MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations

Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them

Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions.

Runs the DEFT embed-then-mine workflow for VCN AOI iterations — embeds the gap-analysis target parquet, embeds a source pool, and mines nearest-neighbour source images for downstream augmentation. Use as the immediate next step after `tao-route-visual-changenet-samples` when expanding a real-image augmentation queue from the mining subset.

Real-time stereo depth estimation using FastFoundationStereo (FFS), the distilled bp2 commercial variant of

Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs

PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.)

Grounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for

Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D