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搜索: "model" — 找到 7,120 条结果
tao-train-centerpose
v?
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training
0
1
0
by @nvidia
tao-train-bevfusion
v?
BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view (BEV) space, used in autonomous driving
0
1
0
by @nvidia
tao-train-action-recognition
v?
Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video cl
0
1
0
by @nvidia
tao-run-platform
v?
TAO Execution SDK for submitting and monitoring GPU training jobs on supported platforms (Lepton, Brev, SLURM, local Docker, Kubernetes). Use when the
0
1
0
by @nvidia
tao-run-on-slurm
v?
Remote SLURM GPU cluster execution over SSH with sbatch/srun, Pyxis/Enroot containers, and Lustre-backed results. Use when running TAO training/eval/i
0
1
0
by @nvidia
tao-run-on-local-docker
v?
Local Docker execution for TAO SDK job containers using the host Docker daemon and NVIDIA GPU runtime. Use when running TAO jobs on the current machin
0
1
0
by @nvidia
tao-run-on-lepton
v1
DGX Cloud Lepton managed GPU compute platform with run/status/cancel interface. Use when submitting TAO jobs to DGX Cloud, dispatching training/eval/i
0
1
0
by @nvidia
tao-run-on-kubernetes
v?
Kubernetes execution platform — submits TAO container jobs as single-pod k8s Jobs with NVIDIA GPU scheduling. Use when running on EKS / GKE / AKS / on
0
1
0
by @nvidia
tao-run-on-brev
v?
Brev managed GPU instances with Docker support. Use when running TAO training, evaluation, or inference on Brev GPU instances, managing Brev deploymen
0
1
0
by @nvidia
tao-run-inference-service
v1
Start, query, and stop a network-specific TAO inference microservice ({network_arch}-inference-microservice) by delegating container execution to the
0
1
0
by @nvidia
tao-run-deft-aoi
v?
Run the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models: baseline evaluate, RCA, ingestion of customer
0
1
0
by @nvidia
tao-run-automl-deft-pipeline
v?
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrai
0
1
0
by @nvidia
tao-port-huggingface-model
v?
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipelin
0
1
0
by @nvidia
tao-mine-aoi-images
v?
Runs the DEFT embed-then-mine workflow for VCN AOI iterations — embeds the gap-analysis target parquet, embeds a source pool, and mines nearest-neighb
0
1
0
by @nvidia
tao-list-capabilities
v?
Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests.
0
1
0
by @nvidia
tao-launch-workflow
v?
Shared launch intake for any TAO workflow or action. Use when the user wants to run TAO AutoML, train, evaluate, infer, export, generate TensorRT engi
0
1
0
by @nvidia
tao-generate-video-reasoning-annotations
v1.0
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA
0
1
0
by @nvidia
tao-generate-referring-expressions
v1
Four-step image referring-expression pipeline: turns images plus KITTI bounding-box labels into region descriptions, scene captions, grounded referrin
0
1
0
by @nvidia
tao-generate-image-grounding
v1
Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them to pixel-space bounding boxes via a VLM
0
1
0
by @nvidia
tao-finetune-huggingface-model
v?
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFac
0
1
0
by @nvidia
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