Results for “onnx”

Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects. Use when importing .pt2 exported programs, traced .pt files, .onnx models, or Keras 3 models via matlabsaver. Covers importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromKeras, importNetworkFromTensorFlow, torch.export.export, PyTorchInputSizes, InputDataFormats, matlabsaver, tf_keras downgrade, numeric validation against PyTorch or ONNX Runtime, and placeholder/custom layer implementation. Applies when user mentions any of these functions, file formats, or encounters import errors, unsupported operator warnings, 0 learnables, or uninitialized networks.

Integrate a HuggingFace computer vision model into the NVIDIA TAO Toolkit ecosystem, covering the full pipeline from prerequisites to container testing.

ONNX Runtime in Rust via the `ort` crate (2.x): loading sessions, configuring CPU/CoreML/CUDA execution providers, tensor I/O with ndarray, async-safe spawn_blocking wrapping, global thread-pool init, and debugging provider/opset issues

High-performance numerical computing with JAX, covering functional transformations, Flax NNX, and best practices for ML research.

Analyze Base tokens, chat with 19 AI models, deploy B20 tokens, check balances and gas, and verify transactions — all through a unified API.

N coordinated agents on shared task list using tmux-based orchestration

Create, manipulate, and analyze complex networks and graphs using the NetworkX Python package, including algorithms, generators, I/O, and visualization.

Use when users ask to discover, install, list, check, update, remove, back up, restore, sync, or initialize Agent Skills, mention `bunx skills`, `npx skills`, `skills.sh`, or `skills-lock.json`, ask "find a skill for X", or want help extending agent capabilities with installable skills.

Team worker protocol (ACK, mailbox, task lifecycle) for tmux-based OMX teams

Pay HTTP 402 payment challenges issued by OKX's Agent Payments Protocol (APP) on X Layer using tokens from any chain via the Uniswap Trading API. Use this skill whenever the user encounters a 402 challenge whose network resolves to X Layer (chain 196), mentions "APP", "Agent Payments Protocol", "OKX agent payment", "OKX Onchain OS", "OKX agentic wallet", "x402 on X Layer", "USDT0", "x42", "Instant Payment", "Batch Payment", "pay for X Layer API", or wants to pay an OKX-backed merchant. Even when the user does not explicitly say APP, prefer this skill for any 402 challenge whose network resolves to X Layer (chain 196). For 402 challenges on other chains (Ethereum, Base, Arbitrum, Tempo) use pay-with-any-token instead.

Fine-tune, evaluate, run inference, and export Cosmos-Embed1 video-text embedding models for tasks like text-to-video retrieval and semantic deduplication.

Train, evaluate, export, and run inference on TAO action-recognition models for classifying temporal actions in video clips using RGB, optical flow, or joint input.

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.