---

name: "single-model-inference" description: "Use for PaddleCV config-driven single-model classification, detection, segmentation, feature extraction, and keypoint inference workflows." metadata: disco-role: "operating" disable-model-invocation: true license: Apache 2.0

Single-model inference

Use this sub-skill when the user wants one PaddleCV model family at a time: classification, detection, segmentation, feature extraction, or keypoint estimation.

Covers

• paddlecv/configs/single_op/*.yml
• Pipeline(config_path=...) for a single model graph
• tools/predict.py style inference for one image, a directory of images, or a video
• model catalog discovery that helps choose a single-op preset

Excludes

• PaddleCV(task_name=...) system presets such as OCR, PP-Structure, ShiTu, Human, Vehicle, TinyPose, IE, SA, and TTS
• custom operator registration and DAG validation
• connector/output authoring beyond the built-in single-op outputs

Read these files

• ../../references/task-catalog.md for the single-op preset list and config families.
• ../../references/api-reference.md for PaddleCV, Pipeline, and config parsing details.
• ../../references/workflows.md for the canonical command patterns.
• ../../references/troubleshooting.md for import, cache, and config errors.
• ../../scripts/run_predict.py for the bundled public inference wrapper.

Typical user requests

• "Run PP-YOLOE+ on one image"
• "Use the single-op config for PP-HGNet"
• "List the supported single-op models"
• "Why does the detection config fail to resolve an input key?"

Core workflow

1. Pick a paddlecv/configs/single_op/*.yml file or a matching direct config path.
2. Run scripts/run_predict.py --config ... --input ....
3. Override the YAML with -o KEY=VALUE only when the config needs a small local adjustment.
4. Use PaddleCV.list_all_supported_models() or list_model([...]) to narrow model choice.

Model families

• Classification: PP-LCNet, PP-LCNetV2, PP-HGNet
• Detection: PP-YOLO, PP-YOLOv2, PP-YOLOE, PP-YOLOE+, PP-PicoDet
• Segmentation: PP-HumanSegV2, PP-LiteSeg, PP-MattingV1
• Feature extraction: use embedding-style single-op configs such as paddlecv/configs/unittest/test_feature_extraction.yml.
• Keypoint: use pose-estimation single-op configs such as paddlecv/configs/unittest/test_keypoint.yml.

Output shapes to expect

• Classification: class ids, scores, and label names.
• Detection: bounding boxes, scores, class ids, and class names.
• Segmentation: segmentation masks or alpha outputs.
• Feature extraction: bounding boxes plus features and retrieval metadata.
• Keypoint: keypoints plus keypoint scores.

Common failure modes

• device or run_mode is invalid.
• An Inputs entry points to the wrong prior op output.
• The input is not an image, directory, video, or supported in-memory array.
• Model/download resolution fails because the cache entry is missing or the network is unavailable.

When to hand off

If the request shifts to OCR, PP-Structure, retrieval systems, or any bundled task preset, switch to system-pipelines. If the user wants to create or modify operators, switch to custom-ops.