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name: resnet-training description: "Guides Tencent ML-Images TensorFlow 1.x ResNet graph, pretraining, finetuning, flags, and training troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

ResNet Training

Use this sub-skill when the task is about the Tencent ML-Images ResNet model, ML-Images multi-label pretraining, ImageNet finetuning, training flags, checkpoint restore, or training/runtime diagnostics.

Read first

• Read references/training-workflows.md for pretraining and finetuning recipes, required split directories, and safe command construction.
• Read references/model-reference.md for the ResNet wrapper, supported depths, tensor shapes, data formats, loss behavior, and preprocessing facts.
• Read references/cli-flags.md when converting a shell example or user request into concrete flags.
• Read references/troubleshooting.md before debugging TensorFlow 1.x, source syntax, checkpoint, GPU, or missing-data failures.

Bundled helpers

• scripts/build_train_command.py prints a safe train.py command template for ML-Images pretraining. It does not run training.
• scripts/build_finetune_command.py prints a safe finetune.py command template for ImageNet finetuning. It does not run training and calls out legacy example-script misspellings.
• scripts/resnet_graph_smoke.py checks whether a user's local Tencent ML-Images checkout and TensorFlow 1.x runtime can import flags and models.resnet and build a small graph.

Route by task

• Need TFRecords or data layout first: route to ../data-preparation/SKILL.md. Training reads split directories; it does not create TFRecords itself.
• Build a pretraining command: use scripts/build_train_command.py, then verify the data root has train/ and val/ shards and the class count is 11166.
• Build a finetuning command: use scripts/build_finetune_command.py, then verify ImageNet-style scalar-label TFRecords, class_num=1000, and a compatible checkpoint.
• Inspect the model graph: read references/model-reference.md, then run scripts/resnet_graph_smoke.py --repo-root <checkout> inside a TensorFlow 1.x environment if a local checkout is available.
• Diagnose failures: start with references/troubleshooting.md. Many issues are legacy TensorFlow/Python compatibility rather than user data mistakes.

Do not overclaim

• The public code is single-node and practical training is GPU/long-running. Do not present a CPU smoke or command render as evidence that full ML-Images pretraining was reproduced.
• The README notes that the authors' real ML-Images training used an internal distributed framework that is not released. The generated skill can guide the public single-node code only.
• Benchmark numbers and checkpoint links are provenance facts from the public README. Do not claim they were remeasured unless a separate experiment did so.