Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use EasyOCR to detect and recognize text in images, configure model and language selection, load custom recognition bundles, and troubleshoot DBNet setup.
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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 skill when the task is about extracting text from images, screenshots, scans, or cropped regions with EasyOCR.
pip install easyocr is usually enough.
For GPU runs, install the matching torch and torchvision wheels first.scripts/inspect_runtime.py to confirm the package imports and the
backend choice without downloading model weights.sub-skills/inference/ for ordinary OCR API or CLI usage.sub-skills/custom-models/ for custom recognition bundles or custom
model directories.sub-skills/dbnet/ for detect_network='dbnet18', DBNet import/init,
or DCN compilation issues.easyocr.Reader initialization and the main OCR methods.python -c "import easyocr; print(easyocr.__version__)"
python scripts/inspect_runtime.py --help
references/api-reference.md for public signatures and output shapes.references/cli-reference.md for the CLI flag groups and parser
caveats.references/configuration.md for environment variables, cache paths,
backend selection, and model selection rules.references/troubleshooting.md for install, import, model-cache, and
CLI/runtime quirks.references/repo-provenance.md to check the source commit, package
version, and refresh baseline.sub-skills/inference/ for normal image OCR.sub-skills/custom-models/ for custom recognition bundles.sub-skills/dbnet/ for DBNet detector setup and DCN compilation.sub-skills/inference/sub-skills/custom-models/sub-skills/dbnet/references/troubleshooting.mdeasyocr.Reader and the CLI as the stable public surface; keep lower-
level implementation details in the sub-skill references.还没有评价。欢迎分享你的使用感受。
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
中文简介正在准备中。当前显示原文说明。
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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