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name: inference-evaluation description: "Run checkpoint-backed LaneNet inference, TuSimple batch evaluation, and postprocess troubleshooting." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Inference and Evaluation

Use this sub-skill when the task is to run or debug LaneNet checkpoint inference, TuSimple-style batch evaluation, DBSCAN/lane-fit postprocessing, or output interpretation.

Route here for

• Single-image inference from a TensorFlow checkpoint using the image preprocessing and restore behavior expected by LaneNet.
• Batch inference/evaluation over a TuSimple test_set/clips image tree and saving overlay outputs.
• Diagnosing empty or black lane masks, DBSCAN clustering failures, missing remap files, checkpoint restore mismatches, and headless plotting issues.
• Understanding binary segmentation, instance embedding, clustered mask, fitted lane parameters, and saved overlay outputs.

Route elsewhere

• Dataset conversion, list files, masks, or TFRecord generation: data-preparation.
• Training, checkpoint creation, TensorBoard, or trainer configuration: training.
• Frozen TensorFlow PB or MNN/mobile export: model-export.

Required context before acting

1. Confirm the user has a LaneNet repo checkout or equivalent source tree with lanenet_model/, local_utils/, config/, and the remap file available from the run working directory.
2. Confirm a TensorFlow 1.x-compatible Python environment. TensorFlow 1.15 with CUDA is verified for this skill; CPU can run inference/evaluation functionally but is slower.
3. Confirm the checkpoint path. Pretrained weights are not bundled. The checkpoint argument should normally be the TensorFlow checkpoint base path, not the .index or .data-* shard.
4. For custom images, ask whether TuSimple lane fitting is appropriate. If not, use --with_lane_fit 0 and inspect raw clustered masks before tuning fitted curves.

Bundled references and scripts

• Read references/workflows.md for single-image, batch-evaluation, checkpoint, and output workflows.
• Read references/api-reference.md for postprocessor signatures, return keys, LaneNet inference outputs, and metric-helper notes.
• Read references/troubleshooting.md when inference produces empty masks, checkpoint restore fails, batch paths crash, or plotting blocks.
• Use scripts/test_lanenet.py as the bundled single-image inference wrapper.
• Use scripts/evaluate_lanenet_on_tusimple.py as the bundled TuSimple batch-evaluation wrapper.

Operating rules

• Do not imply pretrained weights ship with the skill or the repo checkout; obtain a checkpoint from training or a user-supplied pretrained download.
• Prefer noninteractive saving in automation. Use display only when the environment has a working GUI and blocking windows are acceptable.
• Keep inference/evaluation runs from the repository root or pass --repo_root to the bundled scripts so repo-relative config and remap loading resolve correctly.
• For custom data, first validate preprocessing, checkpoint compatibility, and binary segmentation before changing DBSCAN or lane-fit settings.