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name: prediction-and-inference description: "Routes EasyCV predictor APIs, batch prediction, feature extraction, and exported-model inference." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Prediction and inference

Use this sub-skill when the task is to run EasyCV inference from Python, a batch file or ODPS table, or an exported raw / JIT / Blade / ONNX-style model.

It covers the predictor family under easycv.predictors and the batch front door python -m easycv.tools.predict.

Read these references first

• references/predictors.md for the major predictor classes and their shared constructor patterns.
• references/batch-prediction.md for file-list and table-based batch inference.
• references/troubleshooting.md for missing extras, model-type mismatches, and shape / label issues.
• Root references/cli-reference.md for the installed-package command front doors.

What belongs here

Include tasks such as:

• running classification, detection, segmentation, pose, OCR, video, ReID, feature-extraction, or BEVFormer inference
• selecting the right predictor class for the exported artifact you already have
• loading raw, JIT, Blade, or ONNX-style model files
• batch prediction over local files, URLs, or ODPS tables
• label-map handling, score thresholds, and topk output shaping
• image-mode issues such as BGR vs RGB

What stays elsewhere

• Training / validation / config selection -> sub-skills/training-and-evaluation/
• Export and optimization of the model artifact -> sub-skills/export-and-optimization/
• Dataset conversion, file layouts, and OSS setup -> sub-skills/data-preparation/

Typical decision flow

1. Decide whether you want a direct predictor or the batch tool.
2. Identify the model family and the artifact format.
3. Check whether you have a config file, label map, or exported sidecar files.
4. Confirm the input form: file path, image array, list file, or ODPS table.
5. Run the smallest safe inference path first, then scale to batches or tables.

Common success signals

• The predictor class matches the model artifact type.
• The input processor accepts the image mode and file format.
• The output processor returns the expected class, detection_boxes, keypoints, feature, or similar fields.
• Batch inference writes the expected output file or table columns.

Common prediction surfaces

• easy_predict is required for the batch-file / table CLI.
• Exported JIT / Blade models usually need their sidecar config or preprocess artifact nearby.
• mode='RGB' is common for PIL-based classification; many detection pipelines remain BGR.
• label_map_path or CLASSES controls readable class names.