MODELS.yaml

# Model manifest for PAF
#
# Records the provenance, checksum and license of every model used by the shipped
# agent (the `code/` ROS 2 packages). One entry under `models:` per model; entries
# under `excluded:` document models that are deliberately NOT used and why.
# Keep this in sync when a model is added, retrained or its download source changes.
#
# A model can carry restrictions inherited from: pretrained initialisation weights,
# training datasets, dataset terms of use, annotation sources, imported training
# code, or redistribution rules on the produced weights. Each field below exists to
# make those facts auditable.

models:
  # -------------------------------------------------------------------------
  # 1. Traffic-light classifier (COMMITTED in this repository)
  # -------------------------------------------------------------------------
  - name: traffic_light_classifier
    version: model_acc_92.48_val_91.88
    description: >
      Small custom CNN that classifies cropped traffic-light images
      (green/yellow/red/back). Trained in-repo via the DVC experiment pipeline.
    path: code/perception/traffic_light_detection/models/model_acc_92.48_val_91.88.pt
    sha256: 4f4860418af5479f45555549b72198a9efd9c77abd04428fbe375fca68dc7f15
    model_license: MIT                 # project-owned weights
    creator: PAF contributors
    copyright_holder: UNA-AuxMe / PAF contributors
    redistribution_allowed: true
    architecture: >
      From-scratch CNN (4 conv layers + linear head). NO pretrained backbone,
      NO external initialisation checkpoint. See
      `code/perception/traffic_light_detection/src/classification_model.py`.
    initial_checkpoint: none
    training_code: code/perception/traffic_light_detection/src/traffic_light_training.py
    training_pipeline: code/perception/traffic_light_detection/dvc.yaml
    training_datasets:
      - name: traffic_light_dataset
        source: generated from the CARLA simulator (RGB camera crops)
        managed_by: DVC (code/perception/traffic_light_detection/dataset.dvc)
        # Dataset is NOT stored in Git; it is materialised via `dvc pull`.
        license: >
          CARLA is MIT-licensed; simulator-generated screenshots used for
          training are project-owned.
        notes: >
          Confirm the dataset contains no third-party images before any
          redistribution of derived weights. As currently produced, all
          training images are rendered by CARLA.
    verification: "sha256sum -c  # recompute and compare the sha256 above"

  # -------------------------------------------------------------------------
  # 2. Object-detection models (AUTO-DOWNLOADED at runtime by vision_node.py)
  #    Default: fasterrcnn_resnet50_fpn_v2. Selected via the `model` ROS param.
  # -------------------------------------------------------------------------
  - name: fasterrcnn_resnet50_fpn_v2
    framework: torchvision
    description: Primary object detector (vehicles/pedestrians/traffic lights).
    auto_downloaded: true
    download_source: https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth
    how_fetched: >
      torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(
      weights=FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT) on first launch of
      vision_node (vision_node.py).
    code_license: BSD-3-Clause  # torchvision
    weights_license: >
      COCO dataset license (permissive for research use). See
      https://pytorch.org/vision/stable/models.html and
      https://cocodataset.org/#termsofuse
    redistribution_allowed: true-with-attribution   # keep torchvision/COCO notices
    alternatives_available_in_code:
      - fasterrcnn_mobilenet_v3_large_320_fpn   # fastest
      - retinanet_resnet50_fpn_v2

  # -------------------------------------------------------------------------
  # 3. Lane detection model (AUTO-DOWNLOADED at runtime by Lanedetection_node.py)
  # -------------------------------------------------------------------------
  - name: yolop
    description: Lane / drivable-area detection.
    auto_downloaded: true
    how_fetched: 'torch.hub.load("hustvl/yolop", "yolop", pretrained=True)'
    download_source: https://github.com/hustvl/YOLOP
    code_license: MIT             # YOLOP repository
    weights_license: MIT          # YOLOP ships pretrained weights under MIT
    redistribution_allowed: true-with-attribution
    notes: >
      Weights are fetched from the upstream repo at runtime and are NOT vendored
      or committed to this repository. Pinning the exact hub commit is a follow-up
      (see "Remaining work" in THIRD_PARTY_LICENSES.md).

# Models that are EXPLICITLY NOT used by the shipped agent.
excluded:
  - name: ultralytics (YOLOv8 / YOLO11)
    reason: >
      AGPL-3.0 license is incompatible with this MIT project for the combined
      work. Removed from code/requirements.txt and vision_node.py. Must NOT be
      reintroduced into the shipped agent. The CI guard
      (.github/workflows/license-check.yml) fails the build if it reappears.
  - name: YOLO-NAS (super-gradients) weights
    reason: >
      Non-commercial / research-only weight license. Only used once for a
      one-off evaluation in doc/perception/experiments; never redistributed.