“model-training”的搜索结果

利用Wi-Fi测量数据训练和评估RuView模型，涵盖姿态估计、跨环境适应、GPU训练，以及将训练后的模型发布到Hugging Face。

• 不使用摄像头或标签训练姿态估计
• 利用摄像头与Wi-Fi配对数据训练
• 让模型适应不同房间和环境

Agent-driven YOLO fine-tuning — annotate, train, export, deploy

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Analyze and apply AssemblyAI secret, region, consent, retention, deletion, and model-training controls. Use when reviewing security or regulated speech workflows. Trigger with "AssemblyAI security", "AssemblyAI privacy", or "data retention".

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Use when generated assets must keep a consistent style, character, or product look and references stop scaling, or when a user asks to train a custom model through the Scenario MCP, fine-tune a LoRA, clone a voice, curate a training dataset, choose a base model, set epochs and sample prompts, estimate training cost, diagnose a trained model that lost its identity or has one epoch, or generate with a trained model. Keywords: custom model, LoRA, dataset curation, epoch previews.

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Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.

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基于ark_sdk的大模型训练任务自动化工具。帮助用户通过自然语言创建并提交方舟模型训练任务，支持用户自备数据的SFT训练、RFT+GRPO和直接GRPO策略，并引导用户完成训练、状态跟踪与评估闭环。使用场景：当用户需要进行大模型SFT监督微调、RLHF训练、GRPO训练、RFT训练，或需要自动化训练流程时触发。

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Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.

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Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.

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Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

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Use when training, tuning, comparing or evaluating a model — choosing the cross-validation scheme, baselines, tuning inside CV, thresholds and calibration, per-slice metrics, feature importance and error analysis

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Design a model training pipeline — algorithm selection, cross-validation, and serialization. Use when asked to "train a model for this", "design a training pipeline", or "which algorithm should we use".

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Audit existing model training code — find reproducibility issues, data leakage, and missing best practices. Use when asked to "audit our training code", "is our training reproducible", or "check for training data leakage".

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Design a hyperparameter tuning strategy for a model — search space, method, and budget. Use when asked to "tune hyperparameters", "define a search space", or "set a tuning budget".

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