Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use the DeepXiv SDK for citation-aware academic and web research through its Python Reader, deepxiv CLI, progressive paper APIs, optional local LangGraph agent, PMC, bioRxiv/medRxiv, and trending workflows.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use this repo skill when a task needs DeepXiv's hosted literature service or its optional local research agent. It teaches a later Researcher how to choose between the Python SDK and CLI, preserve citations and evidence strength, and avoid credential, quota, truncation, and stale-API mistakes.
python -c "import deepxiv_sdk; print(deepxiv_sdk.__version__)" and
deepxiv --help. If the executable lists an older command set, repair the
installation before using route-specific flags.python -m pip install deepxiv-sdk. For the
optional local OpenAI-compatible LangGraph agent use
python -m pip install "deepxiv-sdk[agent]" (or [all]) and install
tiktoken separately if the optional-import probe reports it missing.ask
requires a registered account key and has a separate agentic quota.Reader, hosted agentic search, source/citation handling, progressive
reading, biomedical sources, PMC, and trending signals.Agent, LangGraph tools, provider options, persistent paper context,
budgets, and circuit-breaker behavior.brief first, then
head, one or two sections, or preview; avoid raw/json for routine
screening.agent_search and agent_search_stream distinguish arXiv and web
flags. Preserve arXiv IDs or URLs, separate retrieved sources from sources
cited in the answer, and reject answer_truncated or streamed error output
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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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.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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