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name: lanenet-lane-detection description: "Route LaneNet lane-detection tasks across data preparation, training, inference, and export workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

LaneNet Lane Detection

Use this skill for tasks on the MaybeShewill-CV lanenet-lane-detection repository: TuSimple data preparation, LaneNet training, checkpoint-backed inference and evaluation, or frozen-PB / MNN export.

Quick route map

• Raw TuSimple labels, masks, list files, TFRecords → data-preparation
• Train or resume LaneNet → training
• Run single-image inference or TuSimple batch evaluation → inference-evaluation
• Freeze checkpoints or prepare PB/MNN export → model-export

Read first

• Repository provenance to confirm the source checkout state.
• Configuration overview for repo-root-relative config loading, default paths, and important knobs.
• Troubleshooting for TF 1.x, protobuf, CUDA/cuDNN, placeholder-path, and checkpoint issues.
• Workflow overview when you need the end-to-end path rather than a single stage.
• Environment check for a quick preflight of imports, repo-root resolution, and GPU visibility.

Shared operating assumptions

• The validated runtime path is Python 3.7 with TensorFlow 1.15 GPU support, CUDA 10.0, and cuDNN 7.6.
• Install requirements.txt plus a TensorFlow 1.15-compatible build; if TensorFlow import errors mention protobuf descriptors, pin protobuf<=3.20.x.
• The repo uses repo-root-relative config loading. If you are not already in the repository root, pass --repo_root to the bundled scripts.
• Pretrained weights are not bundled. Training produces checkpoints that inference and export consume.
• The shipped sample list files under data/training_data_example/ still contain placeholder paths; normalize them before using them as real training input.

How to choose a route

• If the task starts with label*.json, gt_image, gt_binary_image, or gt_instance_image, go to data-preparation.
• If the task mentions tusimple_train.tfrecords, checkpoints, mIoU, TensorBoard, or TRAIN.* / SOLVER.* config values, go to training.
• If the task mentions test_lanenet.py, evaluate_lanenet_on_tusimple.py, DBSCAN_EPS, DBSCAN_MIN_SAMPLES, with_lane_fit, or empty masks, go to inference-evaluation.
• If the task mentions freeze_lanenet_model.py, frozen .pb, final_binary_output, final_pixel_embedding_output, or MNN deployment, go to model-export.

Shared conventions

• Run from the repo root when possible, or pass an explicit --repo_root to the bundled wrappers.
• Keep the original source checkout independent from the generated runtime skill: read the bundled sub-skill references and scripts instead of pointing future users back to tools/ or mnn_project/.
• Use the sub-skill references for the detailed commands, config keys, and troubleshooting steps; this root file is only the router.