manifest.bioimage.io.yaml

format_version: 0.2.2
type: collection
name: ZeroCost4Mic Collection
tags: [zero, bioimage.io]
description: "Resources for BioImgage.IO curated by the ZeroCost4Mic team."
authors: []
documentation: https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki
maintainers: []

cite:
  - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021)"
    doi: 10.1038/s41467-021-22518-0

config:
  id: zero
  name: ZeroCostDL4Mic
  version: 1.7.1
  tags:
    - ZeroCostDL4Mic
  logo: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/ZeroCostLogo.png
  icon: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/ZeroCostLogo.png
  splash_title: ZeroCostDL4Mic
  splash_subtitle: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
  splash_feature_list: []
  explore_button_text: Start Exploring
  background_image: static/img/zoo-background.svg
  resource_types:
    - model
    - application
    - dataset
  default_type: application
  url_root: https://raw.githubusercontent.com/oeway/ZeroCostDL4Mic/master


  #------------------------------------- ZeroCostDL4Mic Datasets ---------------------------------------------

collection:
  # see here for the format: https://bioimage.io/#/?show=contribute
  # replace this with your actual dataset
  - type: dataset
    id: Dataset_StarDist_2D_ZeroCostDL4Mic_2D
    name: StarDist (2D) example training and test dataset - ZeroCostDL4Mic
    description: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Johanna Jukkala, Guillaume Jacquemet]
    documentation: >-
      https://doi.org/10.5281/zenodo.3715492
    tags: [StarDist, segmentation, ZeroCostDL4Mic, 2D]
    download_url: https://doi.org/10.5281/zenodo.3715492
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/Stardist_nuclei_masks.png

  - type: dataset
    id: Dataset_Noise2Void_2D_ZeroCostDL4Mic
    name: Noise2Void (2D) example training and test dataset - ZeroCostDL4Mic
    description: Fluorescence microscopy (paxillin-GFP)
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Aki Stubb, Guillaume Jacquemet, Johanna Ivaska]
    documentation: >-
      https://doi.org/10.5281/zenodo.3713315
    tags: [Noise2Void, denoising, ZeroCostDL4Mic, 2D]
    download_url: https://doi.org/10.5281/zenodo.3713315
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/N2V_wiki.png

  - type: dataset
    id: Dataset_Noise2Void_3D_ZeroCostDL4Mic
    name: Noise2Void (3D) example training and test dataset - ZeroCostDL4Mic
    description: Fluorescence microscopy (Lifeact-RFP)
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Guillaume Jacquemet]
    documentation: >-
      https://doi.org/10.5281/zenodo.3713326
    tags: [Noise2Void, denoising, ZeroCostDL4Mic, 3D]
    download_url: https://doi.org/10.5281/zenodo.3713326
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_3D_dataset.png

  - type: dataset
    id: Dataset_CARE_2D_ZeroCostDL4Mic
    name: CARE (2D) example training and test dataset - ZeroCostDL4Mic
    description: Fluorescence microscopy (Lifeact-RFP)
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Guillaume Jacquemet]
    documentation: >-
      https://doi.org/10.5281/zenodo.3713330
    tags: [CARE, denoising, ZeroCostDL4Mic, 2D]
    download_url: https://doi.org/10.5281/zenodo.3713330
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/CARE_wiki.png

  - type: dataset
    id: Dataset_CARE_3D_ZeroCostDL4Mic
    name: CARE (3D) example training and test dataset - ZeroCostDL4Mic
    description: Fluorescence microscopy (Lifeact-RFP)
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Guillaume Jacquemet]
    documentation: >-
      https://doi.org/10.5281/zenodo.3713337
    tags: [CARE, denoising, ZeroCostDL4Mic, 3D]
    download_url: https://doi.org/10.5281/zenodo.3713337
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/CARE_wiki.png

  - type: dataset
    id: Dataset_fnet_3D_ZeroCostDL4Mic
    name: Label-free prediction (fnet) example training and test dataset - ZeroCostDL4Mic
    description: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594)
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Christoph Spahn]
    documentation: >-
      https://doi.org/10.5281/zenodo.3748967
    tags: [fnet, labelling, ZeroCostDL4Mic, 3D]
    download_url: https://doi.org/10.5281/zenodo.3748967
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/Wiki_files/Fnet_exemplary_data_mitochondria.png

  - type: dataset
    id: Dataset_Deep-STORM_ZeroCostDL4Mic
    name: Deep-STORM training and example dataset - ZeroCostDL4Mic
    description: Time-series of simulated, randomly distributed single-molecule localization (SMLM) data (Training dataset). Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin (Example dataset).
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Christophe Leterrier, Romain F. Laine]
    documentation: >-
      https://doi.org/10.5281/zenodo.3959089
    tags: [SMLM, Deep-STORM, ZeroCostDL4Mic, 2D]
    download_url: https://doi.org/10.5281/zenodo.3959089
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_dataset.png

  - type: dataset
    id: Dataset_CycleGAN_ZeroCostDL4Mic
    name: CycleGAN example training and test dataset - ZeroCostDL4Mic
    description: Unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images)
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Guillaume Jacquemet]
    documentation: >-
      https://doi.org/10.5281/zenodo.3941884
    tags: [CycleGAN, ZeroCostDL4Mic]
    download_url: https://doi.org/10.5281/zenodo.3941884
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/BioimageModelZoo/Images/CycleGAN_dataset.png

  - type: dataset
    id: Dataset_pix2pix_ZeroCostDL4Mic
    name: pix2pix example training and test dataset - ZeroCostDL4Mic
    description: Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Guillaume Jacquemet]
    documentation: >-
      https://doi.org/10.5281/zenodo.3941889
    tags: [pix2pix, ZeroCostDL4Mic]
    download_url: https://doi.org/10.5281/zenodo.3941889
    covers:
      - https://github.com/HenriquesLab/ZeroCostDL4Mic/raw/master/BioimageModelZoo/Images/pix2pix_dataset.png

  - type: dataset
    id: Dataset_YOLOv2_ZeroCostDL4Mic
    name: YoloV2 example training and test dataset - ZeroCostDL4Mic
    description: 2D grayscale .png images with corresponding bounding box annotations in .xml  PASCAL Voc format.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors: [Guillaume Jacquemet, Lucas von Chamier]
    documentation: >-
      https://doi.org/10.5281/zenodo.3941908
    tags: [YOLOv2, ZeroCostDL4Mic]
    download_url: https://doi.org/10.5281/zenodo.3941908
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_dataset.png


  - type: dataset
    id: Dataset_StarDist_Fluo_ZeroCostDL4Mic
    name: Combining StarDist and TrackMate example 1 - Breast cancer cell dataset
    description: Fluorescence microscopy of Nuclei (SiR-DNA) and masks obtained via manual segmentation
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Elnaz Fazeli, Nathan H. Roy, Gautier Follain, Romain F. Laine, Lucas von Chamier, Pekka E. Hänninen, John E. Eriksson, Jean-Yves Tinevez, Guillaume Jacquemet. Automated cell tracking using StarDist and TrackMate. bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.22.306233"
        doi: https://doi.org/10.1101/2020.09.22.306233

    authors: [Guillaume Jacquemet]
    documentation: >-
      https://zenodo.org/record/4034976
    tags: [StarDist, ZeroCostDL4Mic]
    download_url: https://zenodo.org/record/4034976
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingfluo_trackmate.png


  - type: dataset
    id: Dataset_StarDist_brightfield_ZeroCostDL4Mic
    name: Combining StarDist and TrackMate example 2 - T cell dataset
    description: Paired brightfield images of migrating T cells and corresponding masks
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Elnaz Fazeli, Nathan H. Roy, Gautier Follain, Romain F. Laine, Lucas von Chamier, Pekka E. Hänninen, John E. Eriksson, Jean-Yves Tinevez, Guillaume Jacquemet. Automated cell tracking using StarDist and TrackMate. bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.22.306233"
        doi: https://doi.org/10.1101/2020.09.22.306233

    authors: [Nathan H. Roy, Guillaume Jacquemet]
    documentation: >-
      https://zenodo.org/record/4034929
    tags: [StarDist, ZeroCostDL4Mic]
    download_url: https://zenodo.org/record/4034929

    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingTcells_trackmate.png

  - type: dataset
    id: Dataset_StarDist_brightfield2_ZeroCostDL4Mic
    name: Combining StarDist and TrackMate example 3 - Flow chamber dataset
    description: Paired brightfield images of cancer cells and corresponding masks
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Elnaz Fazeli, Nathan H. Roy, Gautier Follain, Romain F. Laine, Lucas von Chamier, Pekka E. Hänninen, John E. Eriksson, Jean-Yves Tinevez, Guillaume Jacquemet. Automated cell tracking using StarDist and TrackMate. bioRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.22.306233"
        doi: https://doi.org/10.1101/2020.09.22.306233

    authors: [Gautier Follain, Guillaume Jacquemet]
    documentation: >-
      https://zenodo.org/record/4034939
    tags: [StarDist, ZeroCostDL4Mic]
    download_url: https://zenodo.org/record/4034939
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingflo_trackmate.png


  - type: dataset
    id: Dataset_StarDist_fluo2_ZeroCostDL4Mic
    name: training dataset for automated tracking of MDA-MB-231 and BT20 cells
    description: Fluorescence microscopy of Nuclei (SiR-DNA) and masks obtained via manual segmentation
    cite:
      - text: "Moreno-Layseca P, Jäntti NZ, Godbole R, Sommer C, Jacquemet G, Al-Akhrass H, Conway JRW, Kronqvist P, Kallionpää RE, Oliveira-Ferrer L, Cervero P, Linder S, Aepfelbacher M, Zauber H, Rae J, Parton RG, Disanza A, Scita G, Mayor S, Selbach M, Veltel S, Ivaska J. Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis. Nat Cell Biol. 2021 Oct;23(10):1073-1084. doi: 10.1038/s41556-021-00767-x. Epub 2021 Oct 6. PMID: 34616024."
        doi: https://doi.org/10.1038/s41556-021-00767-x

    authors: [Hussein Al-Akhrass, Johanna Ivaska, Guillaume Jacquemet]
    documentation: >-
      https://zenodo.org/record/4811213
    tags: [StarDist, ZeroCostDL4Mic]
    download_url: https://zenodo.org/record/4811213
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/Moreno_dataset_stardist.png


  - type: dataset
    id: Dataset_Noisy_Nuclei_ZeroCostDL4Mic
    name: Noisy nuclei dataset.
    description: This dataset contains a denoising training and test dataset for deep learning applications. The training dataset comprises 20 paired matching noisy and high signal-to-noise images. The test dataset contains five paired matching noisy and high signal-to-noise images. Images are Fluorescence microscopy (SiR-DNA) images acquired using a spinning disk confocal microscope with a 20x 0.8 NA objective.
    cite:
      - text: "Laine RF, Arganda-Carreras I, Henriques R, Jacquemet G. Avoiding a replication crisis in deep-learning-based bioimage analysis. Nat Methods. 2021 Oct;18(10):1136-1144. doi: 10.1038/s41592-021-01284-3. PMID: 34608322; PMCID: PMC7611896. "
        doi: https://doi.org/10.1038/s41592-021-01284-3

    authors: [Guillaume Jacquemet]
    documentation: >-
      https://zenodo.org/record/5750174
    tags: [denoising, CARE, Noise2Void, DecoNoising, ZeroCostDL4Mic]
    download_url: https://zenodo.org/record/5750174
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/NoisyNuclei_dataset.png


  #------------------------------------- DeepBacs Datasets ---------------------------------------------

  - type: dataset
    id: Dataset_U-Net_2D_multilabel_DeepBacs
    name: Multi-label U-Net training dataset (Bacillus subtilis) - DeepBacs
    description: Paired bright field images and segmented binary masks of live E. coli cells.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Mia Conduit, Séamus Holden]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [U-Net, multilabel, segmentation, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5639253
    covers:
      - https://zenodo.org/api/iiif/v2/8ba101cf-97ac-4e68-9fff-4cc130b5ea81:93b67447-8b0a-4053-a522-9c141623ca6f:A_Multilabel_U-Net_example_B.subtilis.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_U-Net_2D_DeepBacs
    name: Escherichia coli bright field segmentation dataset - DeepBacs
    description: Paired bright field and segmented mask images of live E. coli cells imaged under bright field.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Spahn Christoph, Heilemann Mike]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [U-Net, segmentation, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5550935
    covers:
      - https://zenodo.org/api/iiif/v2/c6dca42a-62cd-423d-8216-b2ae20dab6a3:790a798b-26b2-425b-b1b6-9545d89daa7d:A_Segmentation_E.coli_large_FoV.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_StarDist_2D_DeepBacs
    name: Mixed segmentation dataset - DeepBacs
    description: Mixed training and test images of S. aureus, E. coli and B. subtilis for cell segmentation using StarDist.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Christoph Spahn, Mike Heilemann, Mia Conduit, Séamus Holden, Pedro Matos Pereira, Mariana Pinho]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [StarDist, segmentation, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5551009
    covers:
      - https://zenodo.org/api/iiif/v2/c6dca42a-62cd-423d-8216-b2ae20dab6a3:790a798b-26b2-425b-b1b6-9545d89daa7d:A_Segmentation_E.coli_large_FoV.png/full/750,/0/default.png


  - type: dataset
    id: Dataset_SplineDist_2D_DeepBacs
    name: Escherichia coli bright field segmentation dataset - DeepBacs
    description: Training and test images of live E. coli cells imaged under bright field for the task of segmentation.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Christoph Spahn, Mike Heilemann]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [StarDist, segmentation, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5550935
    covers:
      - https://zenodo.org/api/iiif/v2/c6dca42a-62cd-423d-8216-b2ae20dab6a3:790a798b-26b2-425b-b1b6-9545d89daa7d:A_Segmentation_E.coli_large_FoV.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_Noise2Void_2D_subtilis_DeepBacs
    name: Bacillus subtilis denoising dataset - DeepBacs
    description: Live-cell time series of vertically aligned B. subtilis cells expressing FtsZ-GFP protein fusion.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Mia Conduit, Séamus Holden]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [Noise2Void, denoising, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5551135
    covers:
      - https://zenodo.org/api/iiif/v2/effa726a-dcdb-47a5-bd2e-63a4a6648ca7:aad7cd51-d389-4cf3-a84e-2d93d1891f20:B.subtilis_VerCINI_examples.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_CARE_2D_coli_DeepBacs
    name: Escherichia coli nucleoid denoising dataset - DeepBacs
    description: Paired training and test images of H-NS-mScarlet-I expressing E. coli cells for image denoising.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Christoph Spahn, Mike Heilemann]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [Noise2Void, CARE, denoising, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5551112
    covers:
      - https://zenodo.org/api/iiif/v2/0417c6d1-0fe3-4d48-bc5a-dfaee6398433:532d7c49-5961-4d61-8ba2-a040f2f19cfd:A_E.coli_H-NS_mScarlet_examples.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_fnet_DeepBacs
    name: Artificial labeling of E. coli membranes dataset - DeepBacs
    description: Training and test images of E. coli cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Christoph Spahn, Mike Heilemann]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [fnet, CARE, artificial labelling, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5551123
    covers:
      - https://zenodo.org/api/iiif/v2/850bd89d-e7ca-4009-80ad-865819df1b6d:c18f2e19-3761-4852-8034-4cefcf7ca318:Artificial_labeling.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_YOLOv2_coli_DeepBacs
    name: Escherichia coli growth stage object detection dataset - DeepBacs
    description: Training and test images of E. coli cells for object detection and classification.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Christoph Spahn, Mike Heilemann]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [YOLOv2, object detection, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5551016
    covers:
      - https://zenodo.org/api/iiif/v2/e30f5766-cc61-4a7c-9d36-30681f2dcbc2:677b2046-5d0b-49df-b231-b5470b6fcbc8:A_Object_detection_cell_cycle.png/full/750,/0/default.png

  - type: dataset
    id: Dataset_YOLOv2_antibiotic_DeepBacs
    name: Escherichia coli antibiotic phenotyping object detection dataset - DeepBacs
    description: Training and test images of E. coli cells treated with different antibiotics for antibiotic phenotyping.
    cite:
      - text: "Christoph Spahn, Romain F. Laine, Pedro Matos Pereira, Estibaliz Gómez-de-Mariscal, Lucas von Chamier, Mia Conduit, Mariana Gomes de Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques DeepBacs: Bacterial image analysis using open-source deep learning approaches. bioRxiv 2021.11.03.467152; doi: https://doi.org/10.1101/2021.11.03.467152"
        doi: https://doi.org/10.1101/2021.11.03.467152
    authors: [Christoph Spahn, Mike Heilemann]
    documentation: https://github.com/HenriquesLab/DeepBacs/wiki
    tags: [YOLOv2, object detection, DeepBacs, 2D]
    download_url: https://zenodo.org/record/5551057
    covers:
      - https://zenodo.org/api/iiif/v2/5b4a4304-ea44-47ce-9c75-4a069bfc9536:e3c27642-4605-42c3-ad3c-1e120917b97e:Antibiotic_profiling_examples.png/full/750,/0/default.png

  #------------------------------------- Notebooks ---------------------------------------------

  - type: application
    id: notebook_preview
    source: https://raw.githubusercontent.com/bioimage-io/nbpreview/master/notebook-preview.imjoy.html

  - type: application
    id: Notebook_U-Net_2D_ZeroCostDL4Mic
    name: U-Net (2D) - ZeroCostDL4Mic
    description: 2D binary segmentation. U-Net is an encoder-decoder architecture originally used for image segmentation. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597"
        url: https://arxiv.org/abs/1505.04597

    authors:
      - Romain Laine and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/2D_Unet_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, U-Net, segmentation, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_U-Net_2D_DeepBacs
    version: 2.2.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: U-Net_2D_DL4Mic is the conversion of the 2D U-Net from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: u-net_2d_zerocostdl4mic-z2.2.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 2.2.1
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/2D_UNet_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_U-Net_3D_ZeroCostDL4Mic
    name: U-Net (3D) - ZeroCostDL4Mic
    description: 3D binary segmentation. The 3D U-Net was first introduced by Çiçek et al for learning dense volumetric segmentations from sparsely annotated ground-truth data building upon the original U-Net architecture by Ronneberger et al. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. https://arxiv.org/abs/1606.06650"
        url: https://arxiv.org/abs/1606.06650

    authors:
      - Daniel Krentzel and Estibaliz Gómez de Mariscal andthe ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/3D_Unet_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_3D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, U-Net, segmentation, ZeroCostDL4Mic, 3D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_3D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 2.2.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: U-Net_3D_DL4Mic is the conversion of the 3D U-Net from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: u-net_3d_zerocostdl4mic-z2.2.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_3D_ZeroCostDL4Mic.ipynb
        notebook_version: 2.2.1
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/3D_UNet_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_StarDist_2D_ZeroCostDL4Mic
    name: StarDist (2D) - ZeroCostDL4Mic
    description: 2D instance segmentation of oval objects (ie nuclei). StarDist is a deep-learning method that can be used to segment cell nuclei in 2D (xy) single images or in stacks (xyz). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Uwe Schmidt, Martin Weigert, Coleman Broaddus, Gene Myers. Cell Detection with Star-Convex Polygons. MICCAI 2018 (2018). https://doi.org/10.1007/978-3-030-00934-2_30"
        doi: https://doi.org/10.1007/978-3-030-00934-2_30

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/2D_Stardist_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingTcells_trackmate.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingflo_trackmate.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/StarDist_trainingfluo_trackmate.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/StarDist_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, StarDist, segmentation, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/StarDist_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_StarDist_2D_ZeroCostDL4Mic_2D
      - Dataset_StarDist_Fluo_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield2_ZeroCostDL4Mic
      - Dataset_StarDist_fluo2_ZeroCostDL4Mic
      - Dataset_StarDist_2D_DeepBacs
    version: '1.20.2'
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: StarDist_2D_DL4Mic is the conversion of the 2D StarDist from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: stardist_2d_zerocostdl4mic-z1.20.2-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/StarDist_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.20.2
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/StarDist_2D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_StarDist_3D_ZeroCostDL4Mic
    name: StarDist (3D) - ZeroCostDL4Mic
    description: 3D instance segmentation of oval objects (ie nuclei). StarDist is a deep-learning method that can be used to segment cell nuclei in 3D (xyz) images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, Gene Myers. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. \tarXiv. https://arxiv.org/abs/1908.03636"
        url: https://arxiv.org/abs/1908.03636

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/StarDist_3D.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/StarDist_3D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, StarDist, segmentation, ZeroCostDL4Mic, 3D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/StarDist_3D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 1.15.3
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: StarDist_3D_DL4Mic is the conversion of the 3D StarDist from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: stardist_3d_zerocostdl4mic-z1.15.3-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/StarDist_3D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.15.3
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/StarDist_3D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Noise2Void_2D_ZeroCostDL4Mic
    name: Noise2Void (2D) - ZeroCostDL4Mic
    description: self-supervised denoising of 2D images. Noise2Void 2D is deep-learning method that can be used to denoise 2D microscopy images. By running this notebook, you can train your own network and denoise your images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "A. Krull, T. Buchholz and F. Jug, Noise2Void Learning Denoising From Single Noisy Images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2124-2132, https://doi.org/10.1109/CVPR.2019.00223."
        doi: https://doi.org/10.1109/CVPR.2019.00223

    authors:
      - Romain Laine and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_2D_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V2D_notebook_3.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Noise2Void_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Noise2Void, denoising, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Noise2Void_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_Noise2Void_2D_ZeroCostDL4Mic
      - Dataset_Noise2Void_2D_subtilis_DeepBacs
      - Dataset_Noisy_Nuclei_ZeroCostDL4Mic
      - Dataset_CARE_2D_coli_DeepBacs
    version: 1.16.2
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: Noise2Void_2D_DL4Mic is the conversion of the 2D Noise2Void from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: noise2void_2d_zerocostdl4mic-z1.16.2-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Noise2Void_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.16.2
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/Noise2Void_2D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Noise2Void_3D_ZeroCostDL4Mic
    name: Noise2VOID (3D) - ZeroCostDL4Mic
    description: self-supervised denoising of 3D images. Noise2VOID 3D is deep-learning method that can be used to denoise 3D microscopy images. By running this notebook, you can train your own network and denoise your images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "A. Krull, T. Buchholz and F. Jug. Noise2Void Learning Denoising From Single Noisy Images. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2124-2132, https://doi.org/10.1109/CVPR.2019.00223."
        doi: https://doi.org/10.1109/CVPR.2019.00223

    authors:
      - Romain Laine and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_3D_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/N2V_3D_notebook_2.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Noise2Void_3D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Noise2Void, denoising, ZeroCostDL4Mic, 3D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Noise2Void_3D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_Noise2Void_3D_ZeroCostDL4Mic
    version: 1.16.2
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: Noise2Void_3D_DL4Mic is the conversion of the 3D Noise2Void from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: noise2void_3d_zerocostdl4mic-z1.16.2-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Noise2Void_3D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.16.2
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/Noise2Void_3D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_CARE_2D_ZeroCostDL4Mic
    name: CARE (2D) - ZeroCostDL4Mic
    description: Supervised restoration of 2D images. CARE is a neural network capable of image restoration from corrupted bio-images, first published in 2018 by Weigert et al. in Nature Methods. The network allows image denoising and resolution improvement in 2D and 3D images, in a supervised training manner. The function of the network is essentially determined by the set of images provided in the training dataset. For instance, if noisy images are provided as input and high signal-to-noise ratio images are provided as targets, the network will perform denoising. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Weigert, M., Schmidt, U., Boothe, T. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15, 1090–1097 (2018). https://doi.org/10.1038/s41592-018-0216-7"
        doi: https://doi.org/10.1038/s41592-018-0216-7

    authors:
      - Lucas von Chamier and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/CARE2D_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/CARE2D_notebook_2.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/CARE2D_notebook_3.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/CARE_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, CARE, denoising, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CARE_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_CARE_2D_ZeroCostDL4Mic
      - Dataset_Noisy_Nuclei_ZeroCostDL4Mic
      - Dataset_CARE_2D_coli_DeepBacs
      - Dataset_fnet_DeepBacs
    version: 1.15.2
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: CARE_2D_DL4Mic is the conversion of the 2D CARE from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.0.0
        docker_hub_image: care_2d_zerocostdl4mic-z1.15.2-d2.0.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CARE_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.15.2
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/CARE_2D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_CARE_3D_ZeroCostDL4Mic
    name: CARE (3D) - ZeroCostDL4Mic
    description: Supervised restoration of 3D images. CARE is a neural network capable of image restoration from corrupted bio-images, first published in 2018 by Weigert et al. in Nature Methods. The network allows image denoising and resolution improvement in 2D and 3D images, in a supervised training manner. The function of the network is essentially determined by the set of images provided in the training dataset. For instance, if noisy images are provided as input and high signal-to-noise ratio images are provided as targets, the network will perform denoising. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Weigert, M., Schmidt, U., Boothe, T. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15, 1090–1097 (2018). https://doi.org/10.1038/s41592-018-0216-7"
        doi: https://doi.org/10.1038/s41592-018-0216-7


    authors:
      - Lucas von Chamier and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/3D_CARE_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/CARE_3D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, CARE, denoising, ZeroCostDL4Mic, 3D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CARE_3D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_CARE_3D_ZeroCostDL4Mic
    version: 1.15.3
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: CARE_3D_DL4Mic is the conversion of the 3D CARE from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: care_3d_zerocostdl4mic-z1.15.3-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CARE_3D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.15.3
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/CARE_3D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_fnet_3D_ZeroCostDL4Mic
    name: Label-free Prediction - fnet - (3D) ZeroCostDL4Mic
    description: Paired image-to-image translation of 3D images. Label-free Prediction (fnet) is a neural network used to infer the features of cellular structures from brightfield or EM images without coloured labels. The network is trained using paired training images from the same field of view, imaged in a label-free (e.g. brightfield) and labelled condition (e.g. fluorescent protein). When trained, this allows the user to identify certain structures from brightfield images alone. The performance of fnet may depend significantly on the structure at hand. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Ounkomol, C., Seshamani, S., Maleckar, M.M. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat Methods 15, 917–920 (2018). https://doi.org/10.1038/s41592-018-0111-2"
        doi: https://doi.org/10.1038/s41592-018-0111-2

    authors:
      - Lucas von Chamier and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/fnet_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/fnet_3D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, fnet, labelling, ZeroCostDL4Mic, 3D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/fnet_3D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_fnet_3D_ZeroCostDL4Mic
    version: 1.13.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: fnet_3D_DL4Mic is the conversion of the 3D fnet from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.0.0
        docker_hub_image: fnet_3d_zerocostdl4mic-z1.13.1-d2.0.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/fnet_3D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.13.1
        python_version: '3.7'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/fnet_3D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_fnet_2D_ZeroCostDL4Mic
    name: Label-free Prediction - fnet - (2D) ZeroCostDL4Mic
    description: Paired image-to-image translation of 2D images. Label-free Prediction (fnet) is a neural network used to infer the features of cellular structures from brightfield or EM images without coloured labels. The network is trained using paired training images from the same field of view, imaged in a label-free (e.g. brightfield) and labelled condition (e.g. fluorescent protein). When trained, this allows the user to identify certain structures from brightfield images alone. The performance of fnet may depend significantly on the structure at hand. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Ounkomol, C., Seshamani, S., Maleckar, M.M. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat Methods 15, 917–920 (2018). https://doi.org/10.1038/s41592-018-0111-2"
        doi: https://doi.org/10.1038/s41592-018-0111-2

    authors:
      - Lucas von Chamier and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/fnet_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/fnet_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, fnet, labelling, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/fnet_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_fnet_DeepBacs
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: fnet_2D_DL4Mic is the conversion of the 2D fnet from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.0.0
        docker_hub_image: fnet_2d_zerocostdl4mic-z1.14.1-d2.0.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/fnet_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.7'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/fnet_2D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Deep-STORM_2D_ZeroCostDL4Mic
    name: Deep-STORM (2D) - ZeroCostDL4Mic
    description: Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data. Deep-STORM is a neural network capable of image reconstruction from high-density single-molecule localization microscopy (SMLM), first published in 2018 by Nehme et al. in Optica. This network allows image reconstruction of 2D super-resolution images, in a supervised training manner. The network is trained using simulated high-density SMLM data for which the ground-truth is available. These simulations are obtained from random distribution of single molecules in a field-of-view and therefore do not imprint structural priors during training. The network output a super-resolution image with increased pixel density (typically upsampling factor of 8 in each dimension). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Elias Nehme, Lucien E. Weiss, Tomer Michaeli, and Yoav Shechtman. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458-464 (2018)"
        doi: https://doi.org/10.1364/OPTICA.5.000458

    authors:
      - Romain Laine and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook_2.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Deep-STORM, labelling, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_Deep-STORM_ZeroCostDL4Mic

    version: 1.13.3
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: Deep-STORM_2D_DL4Mic is the conversion of the 2D Deep-STORM from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: deep-storm_2d_zerocostdl4mic-z1.13.3-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.13.3
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/DeepSTORM_requirements_simple.txt
        sections_to_remove: 2. 6.4.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_pix2pix_2D_ZeroCostDL4Mic
    name: pix2pix (2D) - ZeroCostDL4Mic
    description: Paired image-to-image translation of 2D images. pix2pix is a deep-learning method that can be used to translate one type of images into another. While pix2pix can potentially be used for any type of image-to-image translation, we demonstrate that it can be used to predict a fluorescent image from another fluorescent image. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros. Image-to-Image Translation with Conditional Adversarial Networks. arXiv:1611.07004."
        url: https://arxiv.org/abs/1611.07004

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/pix2pix_notebook_2.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/pix2pix_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/pix2pix_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, pix2pix, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/pix2pix_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_pix2pix_ZeroCostDL4Mic
    version: 1.17.3
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: pix2pix_DL4Mic is the conversion of the pix2pix from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: pix2pix_zerocostdl4mic-z1.17.3-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/pix2pix_ZeroCostDL4Mic.ipynb
        notebook_version: 1.17.3
        python_version: '3.8'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/pix2pix_requirements_simple.txt
        sections_to_remove: 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_CycleGAN_2D_ZeroCostDL4Mic
    name: CycleGAN (2D) - ZeroCostDL4Mic
    description: Unpaired image-to-image translation of 2D images. CycleGAN is a method that can capture the characteristics of one image domain and figure out how these characteristics could be translated into another image domain, all in the absence of any paired training examples (ie transform a horse into zebra or apples into oranges). While CycleGAN can potentially be used for any type of image-to-image translation, we illustrate that it can be used to predict what a fluorescent label would look like when imaged using another imaging modalities. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv:1703.10593"
        url: https://arxiv.org/abs/1703.10593

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cycleGAN_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/CycleGAN_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, CycleGAN, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CycleGAN_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_CycleGAN_ZeroCostDL4Mic
    version: 1.13.3
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: CycleGAN_DL4Mic is the conversion of the CycleGAN from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: cyclegan_zerocostdl4mic-z1.13.3-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/CycleGAN_ZeroCostDL4Mic.ipynb
        notebook_version: 1.13.3
        python_version: '3.8'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/CycleGAN_requirements_simple.txt
        sections_to_remove: 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Augmentor_ZeroCostDL4Mic
    name: Augmentor - ZeroCostDL4Mic
    description: Artificially increase the size of your training dataset. Augmentor is a data augmentation library. Data augmentation can improve training progress by amplifying differences in the dataset. This can be useful if the available dataset is small since, in this case, it is possible that a network could quickly learn every example in the dataset (overfitting), without augmentation. Augmentation can be especially valuable when training dataset need to be manually labelled. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Marcus D Bloice, Peter M Roth, Andreas Holzinger, Biomedical image augmentation using Augmentor, Bioinformatics, Volume 35, Issue 21, 1 November 2019, Pages 4522–4524, https://doi.org/10.1093/bioinformatics/btz259"
        doi: https://doi.org/10.1093/bioinformatics/btz259

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/augmentor_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Tools/Augmentor_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Augmentor, Data Augmentation, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Tools/Augmentor_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: '1.13'
  - type: application
    id: Notebook_DenoiSeg_2D_ZeroCostDL4Mic
    name: DenoiSeg (2D) - ZeroCostDL4Mic
    description: Joint denoising and binary segmentation of 2D images. DenoiSeg 2D is deep-learning method that can be used to jointly denoise and segment 2D microscopy images. The benefits of using DenoiSeg (compared to other Deep Learning-based segmentation methods) are more prononced when only a few annotated images are available. However, the denoising part requires many images to perform well. All the noisy images don't need to be labeled to train DenoiSeg. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Buchholz TO., Prakash M., Schmidt D., Krull A., Jug F. (2020) DenoiSeg: Joint Denoising and Segmentation. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_21"
        doi: https://doi.org/10.1007/978-3-030-66415-2_21

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/Denoiseg_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/DenoiSeg_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, CycleGAN, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DenoiSeg_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: DenoiSeg_DL4Mic is the conversion of the DenoiSeg notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: denoiseg_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DenoiSeg_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/Denoiseg_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Deep-STORM_2D_ZeroCostDL4Mic_DeepImageJ
    name: Deep-STORM (2D) - ZeroCostDL4Mic - DeepImageJ
    description: Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data. Deep-STORM is a neural network capable of image reconstruction from high-density single-molecule localization microscopy (SMLM), first published in 2018 by Nehme et al. in Optica. This network allows image reconstruction of 2D super-resolution images, in a supervised training manner. The network is trained using simulated high-density SMLM data for which the ground-truth is available. These simulations are obtained from random distribution of single molecules in a field-of-view and therefore do not imprint structural priors during training. The network output a super-resolution image with increased pixel density (typically upsampling factor of 8 in each dimension). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these. Networks trained in this notebook can be used in Fiji via deepImageJ and ThunderSTORM plugin.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Elias Nehme, Lucien E. Weiss, Tomer Michaeli, and Yoav Shechtman. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458-464 (2018)"
        doi: https://doi.org/10.1364/OPTICA.5.000458

    authors:
      - Estibaliz Gómez de Mariscal and the deepImageJ and the ZeroCostDL4Mic teams
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DeepSTORM_notebook_2.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/BioImage.io%20notebooks/Deep-STORM_2D_ZeroCostDL4Mic_BioImageModelZoo_export.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Deep-STORM, DeepImageJ, ZeroCostDL4Mic, 2D, deepImageJ, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/BioImage.io%20notebooks/Deep-STORM_2D_ZeroCostDL4Mic_BioImageModelZoo_export.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_Deep-STORM_ZeroCostDL4Mic
    version: '1.13'
  - type: application
    id: Notebook_U-Net_2D_multilabel_ZeroCostDL4Mic
    name: U-Net (2D) multilabel segmentation - ZeroCostDL4Mic
    description: 2D semantic segmentation. U-Net is an encoder-decoder architecture originally used for image segmentation. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597"
        url: https://arxiv.org/abs/1505.04597

    authors:
      - Estibaliz Gómez de Mariscal and the ZeroCostDL4Mic team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/UNet_Multilabel_example.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/UNet-multilabel-example-zoom.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, u-net, segmentation, semantic-segmentation, multilabel, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_U-Net_2D_multilabel_DeepBacs
    version: 2.1.4
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: U-Net_2D_Multilabel_DL4Mic is the conversion of the 2D U-Net Multilabel from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: u-net_2d_multilabel_zerocostdl4mic-z2.1.4-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/U-Net_2D_Multilabel_ZeroCostDL4Mic.ipynb
        notebook_version: 2.1.4
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/2D_UNet_multilabel_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_RCAN_3D_ZeroCostDL4Mic
    name: RCAN (3D) - ZeroCostDL4Mic
    description: Supervised restoration of 3D images. RCAN is a neural network capable of image restoration from corrupted bio-images. The network allows image denoising and resolution improvement in 3D images, in a supervised training manner. The function of the network is essentially determined by the set of images provided in the training dataset. For instance, if noisy images are provided as input and high signal-to-noise ratio images are provided as targets, the network will perform denoising. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Chen, J., Sasaki, H., Lai, H. et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. Nat Methods 18, 678–687 (2021). https://doi.org/10.1038/s41592-021-01155-x"
        doi: https://doi.org/10.1038/s41592-021-01155-x

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/3D_CARE_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/3D_RCAN_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, 3D-RCAN, denoising, ZeroCostDL4Mic, 3D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/3D_RCAN_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_CARE_3D_ZeroCostDL4Mic
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: 3D-RCAN_DL4Mic is the conversion of the 3D-RCAN notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: 3d_rcan_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/3D_RCAN_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/3D_RCAN_requirements_simple.txt
        sections_to_remove: 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_SplineDist_2D_ZeroCostDL4Mic
    name: SplineDist (2D) - ZeroCostDL4Mic
    description: Instance segmentation of 2D images. SplineDist is a neural network inspired by StarDist, capable of performing image instance segmentation. Unlike StarDist, SplineDist uses cubic splines to describe the contour of each object and therefore can potentially segment objects of any shapes. This version is only for 2D dataset. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Soham Mandal, Virginie Uhlmann. SplineDist: Automated Cell Segmentation With Spline Curves. bioRxiv 2020.10.27.357640; doi: https://doi.org/10.1101/2020.10.27.357640"
        doi: https://doi.org/10.1101/2020.10.27.357640

    authors:
      - Romain F. Laine and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Wiki_files/SplineDist_overlay_cropped.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/SplineDist_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, SplineDist, segmentation, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/SplineDist_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_StarDist_2D_ZeroCostDL4Mic_2D
      - Dataset_StarDist_Fluo_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield2_ZeroCostDL4Mic
      - Dataset_StarDist_fluo2_ZeroCostDL4Mic
      - Dataset_StarDist_2D_DeepBacs
      - Dataset_SplineDist_2D_DeepBacs
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: SplineDist2D_DL4Mic is the conversion of the SplineDist 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: splinedist_2d_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/SplineDist_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/SplineDist_2D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_YOLOv2_ZeroCostDL4Mic
    name: YOLOv2 - ZeroCostDL4Mic
    description: Object detection of 2D images. YOLOv2 is an object detection network developed by Redmon & Farhadi, which identifies objects in images and draws bounding boxes around them.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "J. Redmon and A. Farhadi, YOLO9000: Better, Faster, Stronger, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517-6525, doi: 10.1109/CVPR.2017.690."
        doi: https://doi.org/10.1109/CVPR.2017.690
    authors:
      - Lucas von Chamier and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_notebook_2.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/YOLOv2_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, YOLOv2, object detection, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/YOLOv2_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_YOLOv2_ZeroCostDL4Mic
      - Dataset_YOLOv2_coli_DeepBacs
      - Dataset_YOLOv2_antibiotic_DeepBacs
    version: '1.13'
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: YOLOv2_DL4Mic is the conversion of the YOLOv2 from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.0.0
        docker_hub_image: yolov2_zerocostdl4mic-z1.13-d2.0.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/YOLOv2_ZeroCostDL4Mic.ipynb
        notebook_version: '1.13'
        python_version: '3.7'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/YOLO_v2_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Detectron2_ZeroCostDL4Mic
    name: Detectron2 - ZeroCostDL4Mic
    description: Object detection of 2D images. Detectron2 is an object detection network developed by Facebook AI Research, which identifies objects in images and draws bounding boxes around them.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Yuxin Wu and Alexander Kirillov and Francisco Massa and Wan-Yen Lo and Ross Girshick. Detectron2. https://github.com/facebookresearch/detectron2"
        doi: https://github.com/facebookresearch/detectron2

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_notebook_2.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Detectron2_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Detectron2, object detection, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Detectron2_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_YOLOv2_ZeroCostDL4Mic
      - Dataset_YOLOv2_coli_DeepBacs
      - Dataset_YOLOv2_antibiotic_DeepBacs
    version: 1.15.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: Detectron2_2D_DL4Mic is the conversion of the Detectron2 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: detectron2_2d_zerocostdl4mic-z1.15.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Detectron2_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.15.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/Detectron2_2D_requirements_simple.txt
        sections_to_remove: 1.2. 2. 4.2. 6.2.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_DRMIME_ZeroCostDL4Mic
    name: DRMIME - ZeroCostDL4Mic
    description: DRMIME is an network that can be used to register microscopy images (affine and perspective registration).
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Abhishek Nan, Matthew Tennant, Uriel Rubin, Nilanjan Ray. DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration. Proceedings of the Third Conference on Medical Imaging with Deep Learning, PMLR 121:527-543, 2020."
        doi: http://proceedings.mlr.press/v121/nan20a.html

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DrMIME_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/DRMIME_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, DRMIME, image registration, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DRMIME_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: DRMIME_2D_DL4Mic is the conversion of the DRMIME 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: drmime_2d_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DRMIME_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/DRMIME_2D_requirements_simple.txt
        sections_to_remove: 2. 4.4.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Cellpose_2D_ZeroCostDL4Mic
    name: Cellpose (2D and 3D) - ZeroCostDL4Mic
    description: Instance segmentation of 2D and 3D images. Cellpose is a generalist algorithm for cellular segmentation.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). https://doi.org/10.1038/s41592-020-01018-x"
        doi: https://doi.org/10.1038/s41592-020-01018-x

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_2.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_3.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_4.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_5.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Cellpose, Segmentation, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_StarDist_2D_ZeroCostDL4Mic_2D
      - Dataset_StarDist_Fluo_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield2_ZeroCostDL4Mic
      - Dataset_StarDist_fluo2_ZeroCostDL4Mic
    version: 1.16.3
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: Cellpose_2D_DL4Mic is the conversion of the CellPose 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: cellpose_2d_zerocostdl4mic-z1.16.3-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.16.3
        python_version: '3.10'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/Cellpose_2D_requirements_simple.txt
        sections_to_remove: 1.1. 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_RetinaNet_ZeroCostDL4Mic
    name: RetinaNet - ZeroCostDL4Mic
    description: Object detection of 2D images. RetinaNet is a is an object detection network, which identifies objects in images and draws bounding boxes around them.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár. Focal Loss for Dense Object Detection. arXiv:1708.02002"
        url: https://arxiv.org/abs/1708.02002

    authors:
      - Erlantz Calvo, Ignacio Arganda-Carreras and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/yolo_notebook_2.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/RetinaNet_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, RetinaNet, object detection, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/RetinaNet_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_YOLOv2_ZeroCostDL4Mic
      - Dataset_YOLOv2_coli_DeepBacs
      - Dataset_YOLOv2_antibiotic_DeepBacs
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: RetinaNet_DL4Mic is the conversion of the RetinaNet 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: retinanet_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/RetinaNet_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/RetinaNet_requirements_simple.txt
        sections_to_remove: 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_DecoNoising_2D_ZeroCostDL4Mic
    name: DecoNoising (2D) - ZeroCostDL4Mic
    description: Self-supervised denoising of 2D images. DecoNoising 2D is deep-learning method that can be used to denoise 2D microscopy images. By running this notebook, you can train your own network and denoise your images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Anna S. Goncharova, Alf Honigmann, Florian Jug, Alexander Krull. Improving Blind Spot Denoising for Microscopy. arXiv:2008.08414"
        url: https://arxiv.org/abs/2008.08414

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DecoNoising_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/DecoNoising_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, DecoNoising, denoising, ZeroCostDL4Mic, 2D, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DecoNoising_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_Noise2Void_2D_ZeroCostDL4Mic
      - Dataset_Noise2Void_2D_subtilis_DeepBacs
      - Dataset_Noisy_Nuclei_ZeroCostDL4Mic
      - Dataset_CARE_2D_coli_DeepBacs
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: DecoNoising_2D_DL4Mic is the conversion of the DecoNoising 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: deconoising_2d_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DecoNoising_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/DecoNoising_2D_requirements_simple.txt
        sections_to_remove: 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Interactive_Segmentation_Kaibu_2D_ZeroCostDL4Mic
    name: Interactive Segmentation - Kaibu (2D) - ZeroCostDL4Mic
    description: Interactive instance segmentation using Kaibu and Cellpose.
    cite:
      - text: "Ouyang W, Le T, Xu H and Lundberg E. Interactive biomedical segmentation tool powered by deep learning and ImJoy [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:142 (https://doi.org/10.12688/f1000research.50798.1)"
        doi: https://doi.org/10.12688/f1000research.50798.1

      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
    authors:
      - Romain Laine, Wei Ouyang and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_2.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_3.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_4.png
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/cellpose_notebook_5.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/ZeroCostDL4Mic_Interactive_annotations_Cellpose.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Cellpose, Segmentation, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/ZeroCostDL4Mic_Interactive_annotations_Cellpose.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview

    version: 1.13.2
  - type: application
    id: Notebook_MaskRCNN_ZeroCostDL4Mic
    name: MaskRCNN - ZeroCostDL4Mic
    description: Instance segmentation of 2D images. MaskRCNN is a is an object detection and segmentation network, which identifies objects in images and draws bounding boxes around them.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick. Mask R-CNN . arXiv:1703.06870."
        url: https://arxiv.org/abs/1703.06870

    authors:
      - Lucas von Chamier and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/maskRCNN_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/MaskRCNN_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, MaskRCNN, object detection, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/MaskRCNN_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 1.14.2
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: MaskRCNN_DL4Mic is the conversion of the MaskRCNN notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: maskrcnn_zerocostdl4mic-z1.14.2-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/MaskRCNN_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.2
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/MaskRCNN_requirements_simple.txt
        sections_to_remove: 1.2. 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_Quality_Control_ZeroCostDL4Mic
    name: Quality Control - ZeroCostDL4Mic
    description: Error mapping and quality metrics estimation.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Laine, R.F., Arganda-Carreras, I., Henriques, R. et al. Avoiding a replication crisis in deep-learning-based bioimage analysis. Nat Methods 18, 1136–1144 (2021). https://doi.org/10.1038/s41592-021-01284-3"
        doi: https://doi.org/10.1038/s41592-021-01284-3

    authors:
      - Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/QC_notebook.png
    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Tools/Quality_Control_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, Quality Control, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Tools/Quality_Control_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: '1.13'
  - type: application
    id: Notebook_DFCAN_ZeroCostDL4Mic
    name: DFCAN - ZeroCostDL4Mic
    description: Super-resolution via super-pixelisation. Deep Fourier channel attention network (DFCAN) is a network created to transform low-resolution (LR) images to super-resolved (SR) images, published by Qiao, Chang and Li, Di and Guo, Yuting and Liu, Chong and Jiang, Tao and Dai, Qionghai and Li, Dong. The training is done using LR-SR image pairs, taking the LR images as input and obtaining an output as close to SR as posible.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Qiao C, Li D, Guo Y, Liu C, Jiang T, Dai Q, Li D. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat Methods. 2021 Feb;18(2):194-202. doi: 10.1038/s41592-020-01048-5. Epub 2021 Jan 21. PMID: 33479522. "
        doi: https://doi.org/10.1038/s41592-020-01048-5
    authors:
      - Ainhoa Serrano, Ignacio Arganda-Carreras and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/DFCAN_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/DFCAN_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, DFCAN, Super Resolution, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DFCAN_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 1.14.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: DFCAN_DL4Mic is the conversion of the DFCAN 2D notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: dfcan_zerocostdl4mic-z1.14.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/DFCAN_ZeroCostDL4Mic.ipynb
        notebook_version: 1.14.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/DFCAN_requirements_simple.txt
        sections_to_remove: 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: WGAN_ZeroCostDL4Mic.ipynb
    name: WGAN - ZeroCostDL4Mic
    description: Super-resolution via super-pixelisation. Wasserstein GAN (DFCAN) is a network created to transform low-resolution (LR) images to super-resolved (SR) images, published by Gulrajani I. et al. arXiv 2017. The training is done using LR-SR image pairs, taking the LR images as input and obtaining an output as close to SR as posible.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0
      - text: "Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. and Courville, A. Improved Training of Wasserstein GANs. arXiv. 2017 doi: https://doi.org/10.48550/arXiv.1704.00028."
        doi: https://doi.org/10.48550/arXiv.1704.00028
    authors:
      - Ivan Hidalgo, Estibaliz Gómez de Mariscal, Ignacio Arganda-Carreras and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/WGAN_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/WGAN_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, WGAN, Super Resolution, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/WGAN_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
    version: 1.15.1
    config:
      dl4miceverywhere:
        cuda_version: 11.8.0
        cudnn_version: 8.6.0.163
        description: WGAN_DL4Mic is the conversion of the WGAN notebook from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: wgan_zerocostdl4mic-z1.15.1-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/WGAN_ZeroCostDL4Mic.ipynb
        notebook_version: 1.15.1
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/SplineDist_2D_requirements_simple.txt
        sections_to_remove: 2. 6.3.
        ubuntu_version: '22.04'
  - type: application
    id: Notebook_EmbedSeg_2D_ZeroCostDL4Mic
    name: EmbedSeg (2D) - ZeroCostDL4Mic
    description: Instance segmentation of 2D images. EmbedSeg 2D is a deep-learning method that can be used to segment object from bioimages and was first published by Lalit et al. in 2021, on arXiv.
    cite:
      - text: "von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0"
        doi: https://doi.org/10.1038/s41467-021-22518-0

      - text: "Manan Lalit, Pavel Tomancak, Florian Jug. Embedding-based Instance Segmentation in Microscopy. arXiv:2101.10033."
        url: https://arxiv.org/abs/2101.10033

    authors:
      - Amin Rezaei, Guillaume Jacquemet and the ZeroCostDL4Mic Team
    covers:
      - https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/Images/2D_Stardist_notebook.png

    badges:
      - label: Open in Colab
        icon: https://colab.research.google.com/assets/colab-badge.svg
        url: https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/EmbedSeg_2D_ZeroCostDL4Mic.ipynb
    documentation: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/BioimageModelZoo/README.md
    tags: [colab, notebook, EmbedSeg, Segmentation, ZeroCostDL4Mic, dl4miceverywhere]
    download_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/EmbedSeg_2D_ZeroCostDL4Mic.ipynb
    git_repo: https://github.com/HenriquesLab/ZeroCostDL4Mic
    license: MIT
    links:
      - Notebook Preview
      - Dataset_StarDist_2D_ZeroCostDL4Mic_2D
      - Dataset_StarDist_Fluo_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield_ZeroCostDL4Mic
      - Dataset_StarDist_brightfield2_ZeroCostDL4Mic
      - Dataset_StarDist_fluo2_ZeroCostDL4Mic
    version: 1.15.0
    config:
      dl4miceverywhere:
        cuda_version: 11.7.1
        cudnn_version: 8.5.0.96
        description: EmbedSeg_2D_DL4Mic is the conversion of the 2D EmbedSeg from ZeroCostDL4Mic.
        dl4miceverywhere_version: 2.1.0
        docker_hub_image: embedseg_2d_zerocostdl4mic-z1.15.0-d2.1.0
        notebook_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/EmbedSeg_2D_ZeroCostDL4Mic.ipynb
        notebook_version: 1.15.0
        python_version: '3.9'
        requirements_url: https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/requirements_files/EmbedSeg_2D_requirements_simple.txt
        sections_to_remove: 1.0. 1.1. 2.
        ubuntu_version: '22.04'