Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Use CAMEL-AI to build, configure, tool, remember, evaluate, and troubleshoot multi-agent Python systems with self-contained routing across agents, models, tools, RAG, and evaluation workflows.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this repo skill when a task mentions CAMEL-AI, camel-ai, the camel Python package, ChatAgent, RolePlaying, Workforce, CAMEL toolkits, CAMEL model backends, CAMEL memory/RAG, or CAMEL benchmark/datagen/evaluation workflows.
pip install camel-ai for core agents, models, messages, tools, and package imports.camel-ai[model_platforms], camel-ai[rag], camel-ai[storage], camel-ai[document_tools], camel-ai[web_tools], or camel-ai[dev_tools].| Task or signal | Read |
|---|---|
Build a ChatAgent, system/user/assistant messages, task tree, role-playing society, or Workforce | sub-skills/agents-and-societies/SKILL.md |
Configure model providers, ModelFactory, ModelManager, provider enums, local/OpenAI-compatible endpoints, structured output, audio, or multimodal models | sub-skills/models-and-configuration/SKILL.md |
Attach FunctionTool, built-in toolkits, MCP/OpenAPI tools, code execution, interpreters, runtimes, browser/Docker/terminal tools, or CAMEL services | sub-skills/tools-runtimes-and-services/SKILL.md |
| Add memory, retrieval/RAG, embeddings, vector/key-value/object/graph stores, loaders, datahubs, or datasets | sub-skills/memory-rag-and-data/SKILL.md |
| Generate synthetic data, use data collectors, run verifiers/environments, or plan CAMEL benchmarks | sub-skills/datagen-evaluation-and-benchmarks/SKILL.md |
repo-skills-router import process.camel-ai[all] by default in automated environments; select extras by workflow to avoid heavyweight or unsupported optional dependencies.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。