---

name: installation-and-configuration description: "Routes tf-faster-rcnn install, build, CUDA, TensorFlow, Cython NMS, and config-preset questions, including safe environment checks and common failure recovery." disable-model-invocation: true metadata: disco-role: operating license: MIT

Installation and Configuration

Use this sub-skill when a tf-faster-rcnn checkout will not install, build, or import cleanly, or when you need to verify CUDA/TensorFlow/protobuf compatibility, Cython NMS extension readiness, or config preset behavior.

Covered tasks

• set up a legacy Python environment for source inspection or runtime prep
• understand the repo's TensorFlow 1.x / protobuf compatibility window
• build or diagnose lib/setup.py and lib/Makefile
• choose between GPU NMS, CPU NMS, and pure Python inspection
• read and override lib/model/config.py plus experiments/cfgs/*.yml
• run a safe smoke check without downloading data or training

Route elsewhere

• dataset layout, cache, and asset placement -> dataset-and-assets
• demo image inference and checkpoint use -> inference-and-demo
• training, testing, re-evaluation, and TensorBoard -> training-and-evaluation
• backbone and graph internals -> api-and-architecture

Read first

• Install/build notes
• Configuration reference
• Troubleshooting guide

Safe check

Run the bundled inspector from any directory:

• python scripts/check_environment.py --repo-root <tf-faster-rcnn-root>

The inspector only reads local files and imports. It does not download datasets, fetch checkpoints, or start training.

Remember

• cfg.USE_GPU_NMS defaults to True.
• model.nms_wrapper imports both compiled NMS modules eagerly, so cfg.USE_GPU_NMS=False does not by itself make a missing build safe.
• If the inspector reports missing nvcc, CUDAHOME, or model.nms_wrapper, start with the install/build and troubleshooting references before trying demo or training routes.