authors:
- {affiliation: 'School of Informatics, University of Edinburgh', github_user: ThibautGoldsborough,
  name: Thibaut Goldsborough}
cite:
- {doi: 10.48550/arXiv.2408.15954, text: 'If you use InstanSeg for nucleus segmentation
    of brightfield histology images, please cite:'}
- {doi: 10.1101/2024.09.04.611150, text: 'If you use InstanSeg for nucleus and/or
    cell segmentation in fluorescence images, please cite:'}
description: An InstanSeg model trained for instance segmentation
documentation: README.md
format_version: 0.5.3
git_repo: https://github.com/instanseg/instanseg
inputs:
- axes:
  - {type: batch}
  - channel_names: [C1, C2, C3]
    id: channel
    type: channel
  - id: y
    scale: 0.5
    size: {min: 32, step: 1}
    type: space
    unit: micrometer
  - id: x
    scale: 0.5
    size: {min: 32, step: 1}
    type: space
    unit: micrometer
  data: {type: float32}
  id: raw
  preprocessing:
  - id: ensure_dtype
    kwargs: {dtype: float32}
  - id: scale_range
    kwargs:
      axes: [x, y]
      eps: 1e-06
      max_percentile: 99.9
      min_percentile: 0.1
  - id: ensure_dtype
    kwargs: {dtype: float32}
  test_tensor: {sha256: 7249485eca44eefcaf9c8fcd9ce7f8ab5257082c49ecd0e9f62a818e8d4096db,
    source: test-input.npy}
license: Apache-2.0
name: brightfield_nuclei
outputs:
- axes:
  - {type: batch}
  - channel_names: [nuclei]
    type: channel
  - id: y
    scale: 0.5
    size: {axis_id: y, tensor_id: raw}
    type: space
    unit: micrometer
  - id: x
    scale: 0.5
    size: {axis_id: x, tensor_id: raw}
    type: space
    unit: micrometer
  id: instance_segmentation
  test_tensor: {sha256: b927469b00fa4aabd0d5451b71699c146dab817260493222c57e2666902b9708,
    source: test-output_instance_segmentation.npy}
type: model
version: 0.1.0
weights:
  torchscript: {pytorch_version: 2.0.0, sha256: 6dee75ac3d09c7e44d4549a61fe7a3ee058ca489d141c1f9a94bf0c6f039b382,
    source: instanseg.pt}
