"Guides Tencent ML-Images checkpoint-backed classification and
Scanned 9/9/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill checkpoint-inference --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Checkpoint Inference?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-checkpoint-inference)More formats (shields.io, HTML) on the badges page.
---
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](references/workflows.md) for classification and
feature-extraction recipes.
- Read [references/input-output-formats.md](references/input-output-formats.md)
for dictionary, image-list, prediction, and feature result layouts.
- Read [references/troubleshooting.md](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](../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.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!