# family is `deeplab` with version `v3+`, not `deeplabv3plus`: app.js familyOf()
# rendered the old slug as "Deeplabv3plus".
# PARTIAL MODEL. The source checkpoint name ends in
# `custom_seg_split_4_split_2` — this artifact is ONE segment of a 4-way split
# network, not the full end-to-end model. Latency below reflects only that segment;
# do not quote it as whole-model latency.
# PARAMETERS DELIBERATELY ABSENT for exactly that reason: the commonly-cited ~41M
# is the FULL DeepLabV3+/ResNet50 model, so attaching it to one of four segments
# would overstate this artifact several-fold. Count this segment from the graph.

model:
  name: deeplabv3plus-r50
  display_name: DeepLabV3Plus-R50
  # upstream: intentionally absent — these weights have no HuggingFace repo.
  #            See the `source` block below. Never write "TBD" here: the
  #            generator copies it into base_model and renders it as the
  #            model's architecture label in the catalog.
  source:
    kind: openmmlab
    id: deeplabv3plus_r50_d8_4xb2_80k_cityscapes_512x1024
    url: https://github.com/open-mmlab/mmsegmentation/blob/main/configs/deeplabv3plus/metafile.yaml

architecture:
  family: deeplab   # lineage only — no version, no size
  version: "v3+"
  backbone: resnet50-d8
  dataset: cityscapes
  input_resolution: 512x1024
  num_classes: 19
  modality:
    - vision
  # parameters: intentionally absent — see the note above. Fill it with the
  #             exact count instead of an estimate:
  #   import onnx; from onnx import numpy_helper
  #   m = onnx.load('model.onnx')
  #   sum(numpy_helper.to_array(t).size for t in m.graph.initializer)
  parameters_source: unknown

format:
  type: onnx
  # opset: read it off the graph rather than guessing —
  #   onnx.load('model.onnx').opset_import[0].version
  # NOTE: `version` is a GGUF-only field and must not be used for ONNX.

tasks:
  - semantic-segmentation