---

name: tf-faster-rcnn description: "Route TensorFlow 1.x Faster R-CNN tasks for install/build, VOC/COCO assets, pretrained demo inference, training/evaluation, and architecture/API modification in the deprecated tf-faster-rcnn repository." disable-model-invocation: true metadata: disco-role: operating license: MIT

tf-faster-rcnn

Use this repo skill when a Researcher needs task-specific operating guidance for the deprecated tf-faster-rcnn TensorFlow 1.x Faster R-CNN implementation: environment/build triage, VOC/COCO assets, pretrained demo inference, training/evaluation commands, or model/API modifications.

This skill is self-contained guidance derived from source evidence and inspection checks. It does not certify full CUDA/demo/train/test execution; those require external datasets/checkpoints and a compatible TensorFlow 1.x + native-extension stack on the user's target host.

Route by task

User task	Read
Install dependencies, diagnose nvcc, CUDA, TensorFlow/protobuf, Cython extension, nms.gpu_nms, or config override failures	installation-and-configuration
Place/validate VOC or COCO data, demo images, pretrained checkpoint symlinks, ImageNet weights, output folders, or TensorBoard folders	dataset-and-assets
Plan pretrained demo inference, validate demo checkpoint path, understand VOC labels, NMS thresholds, visualization, or CPU/GPU demo caveats	inference-and-demo
Build dry-run train/test/reval/convert commands, choose dataset/net schedule mappings, handle snapshots/resume, TensorBoard, evaluation, and benchmark caveats	training-and-evaluation
Inspect or modify Network, VGG/ResNet/MobileNet backbones, anchors, RPN/proposal layers, bbox transforms, image blobs, roidb/minibatch APIs	api-and-architecture

Start here for common prompts

• "pip install -e . fails" → root is not installable; read install/build notes.
• "nvcc cannot be located" or "nms.gpu_nms missing" → read installation troubleshooting.
• "What --imdb name should I use?" → read data layouts.
• "Where do I put the pretrained ResNet101 VOC07+12 demo model?" → read model artifacts and then demo inference.
• "Generate the VOC07+12 ResNet101 test command" → use training command builder.
• "Change anchor scales or add a backbone" → read architecture notes.

Safe bundled helpers

These helpers are designed to be read-only or dry-run by default:

python sub-skills/installation-and-configuration/scripts/check_environment.py --repo-root <repo-root>
python sub-skills/dataset-and-assets/scripts/validate_layout.py --repo-root <repo-root> --check demo-model
python sub-skills/inference-and-demo/scripts/demo_command_builder.py --repo-root <repo-root> --net res101 --dataset pascal_voc_0712 --validate-only
python sub-skills/training-and-evaluation/scripts/tf_faster_rcnn_command_builder.py test --dataset pascal_voc_0712 --net res101 --gpu-id 0
python sub-skills/api-and-architecture/scripts/inspect_source_api.py --repo-root <repo-root> --strict

Run them from the generated skill directory or pass the script path explicitly. They do not download data, fetch checkpoints, start TensorFlow model execution, or train/evaluate models unless a sub-skill explicitly says otherwise.

Cross-cutting facts

• The project is deprecated and follows TensorFlow r1.x syntax (tf.Session, tf.contrib.slim, tf.py_func, tf.to_int32).
• The repository root has no setup.py/pyproject.toml; native extension setup is under lib/setup.py.
• lib/setup.py requires CUDAHOME or nvcc at setup import time and builds utils.cython_bbox, nms.cpu_nms, and nms.gpu_nms.
• cfg.USE_GPU_NMS defaults to True, and model.nms_wrapper imports both GPU and CPU NMS modules eagerly.
• Default anchor scales/ratios are [8,16,32] and [0.5,1,2]; source inspection verified nine anchors per location.
• Full train/test/demo workflows need external VOC/COCO data, ImageNet or trained checkpoints, old TensorFlow compatibility, and usually a CUDA-capable native build.

Repo-level references

• repo-provenance.md: source commit, dirty-state baseline, package/version facts, and evidence paths for staleness checks.
• troubleshooting.md: cross-sub-skill symptom router for install, data, inference, training, and API failures.
• capability-map.md: coverage/depth summary, verification limits, and which sub-skill owns each capability.

Boundaries

• Do not use this skill for modern TensorFlow 2 object detection, Detectron2/MMDetection, Mask R-CNN/FPN codebases, or multi-GPU TensorPack Faster R-CNN unless the user is explicitly comparing them to this repository.
• Do not claim benchmark AP reproduction from this skill alone. Treat README numbers as historical targets that require native verification.
• Do not import this skill into the live repo-skill library for this production run; the user requested not import.