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name: checkpoint-inference description: "Guides Tencent ML-Images checkpoint-backed classification and feature extraction workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Checkpoint Inference

Use this sub-skill when the task is about running Tencent ML-Images checkpoint inference: top-k single-label classification or feature extraction from a pretrained ResNet checkpoint.

Read first

• Read references/workflows.md for classification and feature-extraction recipes.
• Read references/input-output-formats.md for dictionary, image-list, prediction, and feature result layouts.
• Read references/troubleshooting.md when a checkpoint, dictionary, image, or OpenCV preprocessing step fails.

Bundled helpers

• scripts/inspect_inference_inputs.py validates the image list, dictionary, checkpoint path/prefix, and compatibility settings before printing a safe classification command.
• scripts/inspect_feature_inputs.py validates the image list, checkpoint, and output path before printing a safe feature-extraction command.

Route by task

• Need the model graph or checkpoint compatibility: cross-link to ../resnet-training/SKILL.md.
• Need classification results: use the classification inspector to check the image list, dictionary, and checkpoint. Only then run the printed command in a prepared TensorFlow 1.x/OpenCV runtime.
• Need features: use the feature inspector to confirm the checkpoint and output path, then run the printed command.
• Need training or finetuning: route to the training sub-skill.

Safety notes

• The public example scripts restore checkpoints at top level. Do not import or execute them blindly in a generic Python session; use the bundled inspectors to validate inputs first.
• Checkpoint files are external artifacts. This skill documents the expected prefix/path structure and compatibility checks but does not bundle weights.
• OpenCV is required for preprocessing. If cv2 is missing, install it in the inspection environment or report the missing optional dependency clearly.