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name: tencent-ml-images description: "Guides Tencent ML-Images data preparation, TensorFlow 1.x ResNet training/finetuning, and checkpoint inference workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Tencent ML-Images

Use this repo skill when the task mentions Tencent ML-Images, the ML-Images multi-label dataset, the public ResNet-101 code, TFRecord preparation, ImageNet finetuning, single-label classification, or feature extraction.

Start here

• Read references/setup-and-scope.md for the supported runtime shape, verified smoke environment facts, and what is and is not bundled.
• Read references/repo-provenance.md when you need to confirm whether this skill matches the current checkout.
• Run scripts/check_legacy_env.py for a fast TensorFlow/OpenCV/source smoke before deeper workflow work.
• Use the sub-skill map below to route the request to the narrowest workflow.

Sub-skill map

data-preparation

Use this for ML-Images/OpenImages URL lists, image lists, dictionary files, semantic hierarchy files, TFRecord conversion, and downloader validation. Read sub-skills/data-preparation/SKILL.md.

resnet-training

Use this for the ResNet graph, pretraining, finetuning, flags, training command construction, and training troubleshooting. Read sub-skills/resnet-training/SKILL.md.

checkpoint-inference

Use this for checkpoint-backed top-k classification and feature extraction. Read sub-skills/checkpoint-inference/SKILL.md.

Common route examples

• "I need to turn URLs and local images into TFRecords" → data-preparation.
• "I want a command for ML-Images pretraining or ImageNet finetuning" → resnet-training.
• "I have a checkpoint and want labels or features" → checkpoint-inference.
• "I need to know whether this checkout still matches the skill" → read references/repo-provenance.md first.

Runtime expectations

• The public repository is a legacy TensorFlow 1.x project. The verified smoke used TensorFlow 1.6.0 and OpenCV 4.2 in a CPU inspection environment; a newer TensorFlow 1.15 stack can trip over legacy flag registration in flags.py.
• Do not assume TensorFlow 2-only behavior is compatible with the original model scripts, because the source uses tf.app, tf.contrib, tf.gfile, and tf.python_io-era APIs.
• The README mentions Python 2.7, but the generated skill is organized around the source workflow surface and the verified legacy TensorFlow smoke. Follow the sub-skill troubleshooting pages for the safest runtime choice.

Safe first checks

1. python scripts/check_legacy_env.py
2. If you have a checkout path, rerun with --repo-root <checkout>.
3. Then move to the relevant sub-skill and its bundled helper scripts.

Shared guardrails

• Do not start bulk downloads, long training runs, or checkpoint restores before validating the data layout and compatibility flags.
• Do not use the source checkout as a documentation dependency. The bundled references and scripts should be enough for future agents.
• Do not confuse the public README shell snippets with a safe smoke check. Use the bundled helpers first, then decide whether the actual workflow is worth running.