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name: training-and-evaluation description: "Train, test, re-evaluate, and convert tf-faster-rcnn experiments safely with schedule-aware command construction, config overrides, snapshot handling, output paths, and evaluation caveats." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and Evaluation

Use this sub-skill when a Researcher needs to plan, dry-run, diagnose, or deliberately launch tf-faster-rcnn training, validation, testing, re-evaluation, or deprecated VGG16 snapshot conversion.

Route here for

• Expanding train_faster_rcnn.sh, test_faster_rcnn.sh, and convert_vgg16.sh behavior into explicit tools/trainval_net.py, tools/test_net.py, tools/reval.py, or tools/convert_from_depre.py commands.
• Choosing the repository's dataset schedule mapping for pascal_voc, pascal_voc_0712, or coco, including train/test imdb names, iteration counts, learning-rate step, anchors, and ratios.
• Choosing a supported network selector: vgg16, res50, res101, res152, or mobile, while checking whether a matching config file exists in the user's checkout.
• Applying safe config overrides through the repository's --set KEY VALUE ... grammar and predicting the output tag that shell launchers would derive from extra arguments.
• Reasoning about snapshots, resume behavior, TensorBoard directories, test output directories, re-evaluation from detections.pkl, and deprecated VGG16 conversion.
• Diagnosing missing initialization weights, missing trained checkpoints, NaNs, stale snapshots, NMS/AP mismatches, and expensive-run blockers.

Route elsewhere

• Installation, TensorFlow/CUDA compatibility, Cython extension builds, USE_GPU_NMS, and environment checks belong in installation-and-configuration.
• VOC/COCO directory layouts, dataset registry details, ImageNet initialization checkpoint placement, and pretrained model artifact layouts belong in dataset-and-assets.
• Demo-only image inference and visualization on custom images belongs in inference-and-demo.
• Low-level network, RPN, RoI, bbox, and model-extension APIs belong in api-and-architecture.
• Cross-cutting repo issues should also consult the root troubleshooting reference when it exists: root troubleshooting.

First safety rule

Do not start a real train/test/evaluation run while only trying to answer a planning question. Full training and benchmark evaluation are expensive and require external datasets, checkpoints, a compatible TensorFlow 1.x runtime, and usually compiled CUDA/NMS extensions. Use the bundled dry-run builder first:

python sub-skills/training-and-evaluation/scripts/tf_faster_rcnn_command_builder.py train --dataset pascal_voc_0712 --net res101 --gpu-id 0

The builder prints commands only; it never trains or evaluates by itself.

Read in this order

1. training-evaluation.md for workflow semantics, schedule mappings, output directories, snapshots/resume, TensorBoard, benchmark caveats, and failure recovery.
2. cli-reference.md for command-line options, override grammar, and command-builder examples.
3. troubleshooting.md for targeted diagnosis of missing data/weights, NaNs, stale snapshots, NMS/AP surprises, and expensive-run blocks.

Evidence-backed limits

• Full CUDA build, demo, train, test, and benchmark AP reproduction were not verified during skill production. CPU/source inspection verified command facts, config behavior, dataset registry facts, anchor/NMS utility behavior, and TensorFlow 1.15 CPU import as an inspection substitute for the TensorFlow r1.2-era repository.
• The repository is deprecated; benchmark numbers are historically reported, stochastic, and dependent on old TensorFlow/CUDA/native-extension behavior.
• res152 is accepted by tools/trainval_net.py and tools/test_net.py and appears in README performance notes, but the checked-out experiments/cfgs/ evidence did not include res152.yml; require a user-supplied config or cloned evidence before claiming a ready command.