EasyCV

Use this skill for EasyCV workflows that sit on top of the repo's configuration-driven model zoo, training / evaluation entry points, predictor APIs, data-preparation helpers, and export or optimization paths.

EasyCV is a broad computer-vision toolbox. Keep this root router short and use the focused sub-skills for workflow detail.

Start here

• Read references/installation.md if you need the public install path, optional extras, or a minimal smoke check.
• Read references/model-zoo-overview.md when you need to choose a config family or understand the main task coverage.
• Read references/cli-reference.md when you need the canonical command/module front doors.
• Read references/troubleshooting.md for cross-cutting install, import, backend, config, and OSS issues.
• Run scripts/check_easycv_env.py to confirm the package import surface and optional backend availability in a prepared environment.

Route map

sub-skills/data-preparation/

Use for dataset layouts, annotation conversion, file/OSS I/O, dataset download prep, and repo-maintained conversion helpers.

Typical asks:

• "How do I prepare COCO/ImageNet/VOC/nuScenes data for EasyCV?"
• "How do I convert annotations or check a data layout?"
• "How do I use EasyCV file I/O with local or OSS paths?"

sub-skills/training-and-evaluation/

Use for training, fine-tuning, evaluation, config selection, distributed launch, metric setup, and training-time logging or visualization.

Typical asks:

• "How do I train a classification or detection model?"
• "Which config template should I start from?"
• "How do I run eval or resume a checkpoint?"

sub-skills/prediction-and-inference/

Use for Python predictor APIs, batch prediction, feature extraction, OCR / pose / video / segmentation inference, and exported-model consumption.

Typical asks:

• "How do I run batch inference on images or tables?"
• "Which predictor class should I use?"
• "How do I load a JIT / Blade / ONNX / raw checkpoint for inference?"

sub-skills/export-and-optimization/

Use for model export, JIT / Blade / ONNX packaging, pruning, quantization, TorchAccelerator, and other inference-optimization paths.

Typical asks:

• "How do I export a checkpoint for inference?"
• "How do I prune or quantize a YOLOX model?"
• "What extra packages are needed for Blade or TorchAcc?"

What this skill does not do

• It does not depend on the original checkout remaining available at runtime.
• It does not expose generated-skill content outside the router and bundled references.
• It does not import optional heavyweight extras unless the selected workflow needs them.

Common entry points

The installed package exposes the public workflow modules under easycv.tools and the main APIs under easycv.apis and easycv.predictors.

Use those modules when you want to stay in the installed package rather than the source checkout.

Shared guardrails

• Match the config family to the task before editing paths or hyperparameters.
• Prefer the smallest runnable command first: --help, import checks, or a tiny smoke input.
• Treat advanced backends such as easy_predict, modelscope, pai_nni, blade_compression, torchacc, or nvidia-dali as optional dependencies unless the selected workflow needs them.
• Keep dataset conversion and optimization helpers separate from core training and inference guidance.