“onnx”的搜索结果

使用sherpa-onnx语音模型，在本地将文字转换为朗读音频并保存为WAV文件。准备好运行环境和模型后，无需连接云服务即可生成语音。

• 将文字保存为WAV音频
• 不连接云服务生成语音
• 切换语音模型选择声音

Replace the object detector in VSS Alerts Blueprint verification mode with an ONNX model. This guide covers model placement, interpreting detection outputs, DeepStream configuration, TensorRT engine builds, redeployment, and common loading or mount failures.

中文简介正在准备中。当前显示英语简介。

• Replace the verification detector with a YOLO11 ONNX model
• Investigate DeepStream custom parser loading failures
• Fix ONNX mounts and TensorRT engine rebuild settings

Select technologies for AI and machine learning features in .NET 8+ applications. Get task-specific plans or implementation guidance for structured predictions, text generation, document retrieval, tool-using agents, and running pretrained models locally or in production.

中文简介正在准备中。当前显示英语简介。

• Choosing libraries for classification or prediction in .NET
• Adding text generation or summarization to an application
• Designing document retrieval with source-backed AI answers

Diagnose errors and incorrect inference results in MNN's QNN backend for Qualcomm HTP processors. Compare intermediate calculation data against CPU results to locate the first faulty operator, distinguish bugs from precision limits, and guide operator support and performance fixes.

中文简介正在准备中。当前显示英语简介。

• Investigate QNN runtime and model conversion errors
• Find the first operator producing incorrect results
• Distinguish implementation bugs from numerical precision limits

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.

中文简介正在准备中。当前显示原文说明。

Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.

中文简介正在准备中。当前显示英语简介。

• Build an object detection pipeline
• Clean images and convert annotation formats
• Optimize a trained model for deployment

Generates a standalone YOLO26 detection and tracking service scaffold for VSS Search and Alerts. Includes DeepStream configuration, Docker files, and tests, with Kafka integration guidance and clear limits on validation before model assets are available.

中文简介正在准备中。当前显示英语简介。

• Creating a standalone YOLO26 service for VSS
• Generating a scaffold before model assets are ready
• Checking Kafka integration with VSS workflows

Integrate a supported object detection model from HuggingFace or NVIDIA NGC into NVIDIA DeepStream. Automates model acquisition and conversion, TensorRT engine building, detection output parsing, multi-stream performance benchmarks, and reports with charts in Markdown, HTML, and PDF.

中文简介正在准备中。当前显示英语简介。

• Run the default RT-DETR model in DeepStream
• Integrate a supported HuggingFace or NVIDIA NGC detection model
• Compare performance across multiple video streams

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

中文简介正在准备中。当前显示原文说明。

使用SAM通过点或矩形框指定图像中的对象，生成用于抠图的区域掩码。指导自动生成、边界修正和质量筛选，也涵盖训练数据准备及模型部署步骤。

• 将对象抠出并保留透明背景
• 制作图像区域标注工具
• 准备图像识别训练数据

将Claude Code自动记忆导入AgentDB，按语义跨会话和项目搜索记录，检查导入状态、合并近似重复内容，并在搜索结果中显示记忆来源。

• 跨项目查找过去的记录
• 首次导入所有项目的记忆
• 合并内容相似的记忆

安装固定版本的ruvector@0.3.3及配套软件包，补齐首次使用所需依赖，向Claude注册MCP服务器，并通过诊断命令和冒烟测试检查安装结果。

• 首次安装ruvector
• 补齐Brain、SONA和LLM依赖
• 向Claude注册MCP服务器

将文本、代码和文档转换为用于比较语义的384维数值向量，为搜索和分类做准备，并保存结果文件、检查转换结果及记录元数据。

• 为语义文档搜索做准备
• 为文本或代码相似度比较做准备
• 为相近内容的文档分类做准备

Use SAM to identify object regions in images from points, boxes, or existing masks. The skill provides implementation examples for transparent cutouts, automatic mask generation, annotation tools, and model selection based on available resources.

中文简介正在准备中。当前显示英语简介。

• Extracting objects with transparent backgrounds
• Building annotation tools with point or box selection
• Generating object masks automatically

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

中文简介正在准备中。当前显示英语简介。

• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Create masks that identify object regions in images using points, boxes, or existing masks. The skill guides automatic mask generation, transparent-background extraction, annotation workflows, and deployment, without requiring training for each specific task.

中文简介正在准备中。当前显示英语简介。

• Extracting objects with transparent backgrounds
• Building annotation tools with point and box selection
• Generating object masks automatically

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

中文简介正在准备中。当前显示原文说明。

Provisions the oracle ML inference daemon with onnxruntime via uv. Use when setting up local ONNX model inference for skill quality evaluation.

中文简介正在准备中。当前显示原文说明。

Patterns for running AI models locally in browsers using WebGPU, Transformers.js, WebLLM, and ONNX Runtime. Zero API costs, full privacy. Use when "on-device AI, browser AI, WebLLM, Transformers.js, WebGPU, edge inference, offline AI, client-side ML, ONNX web, " mentioned.

中文简介正在准备中。当前显示原文说明。

Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

中文简介正在准备中。当前显示原文说明。

Local text-to-speech via sherpa-onnx (offline, no cloud)

中文简介正在准备中。当前显示原文说明。

Expert en IA embarquée (TensorFlow Lite, ONNX, quantization, deployment on edge devices)

中文简介正在准备中。当前显示原文说明。

Extract text from PDFs, Office documents, images, spreadsheets, and text files through one tool. Results share a common JSON structure, supporting document summaries, contract checks, and spreadsheet previews, with optical character recognition for scanned content.

中文简介正在准备中。当前显示英语简介。

• Summarizing papers and reports
• Checking key clauses in Word contracts
• Previewing every sheet in an Excel workbook

Cut people or products out of photos and save them as transparent PNGs, place them on solid colors, or combine them with another background image. Choose general or portrait models, with optional edge refinement for hair and fur.

中文简介正在准备中。当前显示英语简介。

• Create product photos with white backgrounds
• Extract a person as a transparent PNG
• Place a subject on another background image