---

name: inference-and-demo description: "Plan tf-faster-rcnn pretrained demo/image inference, checkpoint selection, NMS behavior, visualization, and dry-run command building without running the model." disable-model-invocation: true metadata: disco-role: operating license: MIT

tf-faster-rcnn Inference and Demo

Use this sub-skill for the repo's pretrained demo flow: choosing the backbone/dataset pair, validating the TensorFlow checkpoint prefix, reasoning about tools/demo.py and lib/model/test.py, and diagnosing inference-time image, visualization, and NMS failures.

Route here for

• Building or explaining the stock demo command for the bundled sample images.
• Deciding between vgg16 and res101 for tools/demo.py.
• Checking the checkpoint prefix and .meta file before demo runs.
• Understanding the VOC class list, demo thresholds, and output window behavior.
• Debugging cv2.imread, plt.show(), or ResNet101 memory pressure during demo.

Do not handle here

• Dataset folder creation, checkpoint downloads, or symlink layout: use ../dataset-and-assets/SKILL.md.
• Native extension builds, nms.gpu_nms installation, CUDA/toolkit setup, or USE_GPU_NMS build recovery: use ../installation-and-configuration/SKILL.md.
• Dataset-wide AP runs, tools/test_net.py, tools/reval.py, or training/evaluation launchers: use ../training-and-evaluation/SKILL.md.
• Backbone internals, feature-map shapes, or custom architecture changes: use ../api-and-architecture/SKILL.md.

Fast path

1. Confirm the checkpoint family matches the requested network/dataset pair.
2. Validate the TensorFlow checkpoint prefix under output/<net>/<train_imdb>/default/<snapshot>.ckpt.
3. Remember the demo script only supports --net {vgg16,res101} and --dataset {pascal_voc,pascal_voc_0712}.
4. Use the bundled command builder to print a safe command and validation result without running inference.
5. Expect a matplotlib window after each bundled demo image; on headless systems, use an off-screen backend or save figures in a local copy of the script.

Bundled references

• references/demo-inference.md: demo control flow, selectors, class labels, command shapes, and output expectations.
• references/nms-and-postprocessing.md: NMS dispatch, threshold handling, bbox postprocessing, and import caveats.
• references/troubleshooting.md: checkpoint, OpenCV, NMS, visualization, and memory failure modes.

Bundled helper

• scripts/demo_command_builder.py prints a shell command for the demo script and validates the expected checkpoint files.
• It accepts --repo-root, --net, --dataset, --gpu-id, --cpu, and --validate-only.
• It never downloads assets, trains, or runs inference.

Key facts to keep straight

• tools/demo.py hardcodes the five sample images under data/demo/.
• The demo uses the Pascal VOC class list: background plus 20 foreground labels.
• Demo thresholds are fixed in source: CONF_THRESH = 0.8 and NMS_THRESH = 0.3.
• cfg.USE_GPU_NMS=False changes dispatch, but it does not remove the top-level nms.gpu_nms import in nms_wrapper.py.
• lib/model/test.py is the shared im_detect/test_net core; the demo flow is a thin wrapper around it.