PK-YOLO

Model Card for Model ID mkang315/PK-YOLO

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Model Details

Model Description

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• Developed by: Ming Kang
• Funded by Monash University: Faculty of Information Technology (FIT)
• Shared by Ming Kang: Affirom Monash FIT
• Model type: Pretrained medical image model, pretrained brain MRI image weights, brain tumor detection
• License: GNU General Public License 3.0
• Pretrained from models RepViT-M2.3 and Sparse masKed modeling (SparK): Pretrained RepViT via SparK as YOLOv9-E backbone

Model Sources

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• Repository: Github
• Paper (published): PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplane MRI Slices
• CVF: WACV 2025 open access provided by the Computer Vision Foundation
• IEEE Xplore: Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)
• ArXiv: arxiv:2410.21822
• Daily Papers: hf:2410.21822

Uses

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Installation

Install requirements.txt in a Python>=3.8.0 environment, including PyTorch>=1.7.0.

pip install -r requirements.txt

Direct Use

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python train_dual.py

Downstream Use

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from huggingface_hub import hf_hub_download

filepath = hf_hub_download(repo_id="mkang315/PK-YOLO", filename="spark repvit/repvit_1kpretrained_timm_style.pth")

<!-- ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- [More Information Needed] <!-- ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> <!-- [More Information Needed] <!-- ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> <!-- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. <!-- ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] --> <!-- ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> <!-- [More Information Needed] <!-- ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> <!-- #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> <!-- #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> <!-- [More Information Needed] <!-- ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> <!-- ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> <!-- [More Information Needed] <!-- #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> <!-- [More Information Needed] <!-- #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> <!-- [More Information Needed] <!-- ### Results [More Information Needed] <!-- #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> <!-- [More Information Needed] <!-- ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> <!-- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/arxiv.1910.09700). --> <!-- - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] -->

Citation

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Please cite the paper if using this model. Here is a guide to referencing this work in various styles for formatting your references:

BibTeX:

\begin{thebibliography}{1}
\bibitem{Kang25Pkyolo} M. Kang, F. F. Ting, R. C.-W. Phan, and C.-M. Ting, "Pk-yolo: Pretrained knowledge guided yolo for brain tumor detection in multiplane mri slices," in {\emph Proc. Winter Conf. Appl. Comput. Vis. (WACV)}, Tucson, AZ, USA, Feb. 28--Mar. 4, 2025, pp. 3732--3741.
\end{thebibliography}

@inproceedings{Kang25Pkyolo,
  author = "Ming Kang and Fung Fung Ting and Rapha{\"e}l C.-W. Phan and Chee-Ming Ting",
  title = "Pk-yolo: Pretrained knowledge guided yolo for brain tumor detection in multiplane mri slices",
  booktitle = "Proc. Winter Conf. Appl. Comput. Vis. (WACV)",
  % booktitle = WACV, %% IEEE Full Name Reference Style
  address = "Tucson, AZ, USA, Feb. 28--Mar. 4",
  pages = "3732--3741",
  year = "2025"
}

@inproceedings{Kang25Pkyolo,
  author = "Kang, Ming and Ting, Fung Fung and Phan, Rapha{\"e}l C.-W. and Ting, Chee-Ming",
  title = "{PK-YOLO}: pretrained knowledge guided {YOLO} for brain tumor detection in multiplane {MRI} slices",
  editor = "",
  booktitle = "2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
  series = "",
  volume = "",
  pages = "3732--3741",
  publisher = "IEEE",
  address = "Piscataway",
  year = "2025",
  doi= "10.1109/WACV61041.2025.00367",
  url = "https://doi.org/10.1109/WACV61041.2025.00367"
}

IEEE Full Name Reference Style: Ming Kang, Fung Fung Ting, Raphaël C.-W. Phan, and Chee-Ming Ting. Pk-yolo: Pretrained knowledge guided yolo for brain tumor detection in multiplane mri slices. In WACV, pages 3732–3741, 2025.</br> ^{NOTE: This is a modification to the standard IEEE Reference Style and used by most IEEE/CVF conferences, including CVPR, ICCV, and WACV, to render first names in the bibliography as "Firstname Lastname" rather than "F. Lastname" or "Lastname, F.", which the reference styles of NeurIPS, ICLR, and IJCAI are similar to.}

IEEE Reference Style: M. Kang, F. F. Ting, R. C.-W. Phan, and C.-M. Ting, "Pk-yolo: Pretrained knowledge guided yolo for brain tumor detection in multiplane mri slices," in Proc. Winter Conf. Appl. Comput. Vis. (WACV), Tucson, AZ, USA, Feb. 28–Mar. 4, 2025, pp. 3732–3741.</br> ^{NOTE: City of Conf., Abbrev. State, Country, Month & Day(s) are optional.}

Nature Reference Style: Kang, M., Ting, C.-M., Ting, F. F. & Phan, R. C.-W. PK-YOLO: pretrained knowledge guided YOLO for brain tumor detection in multiplane MRI slices. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 3732–3741 (IEEE, 2025).</br>

Springer Reference Style: Kang, M., Ting, F.F., Phan, R.C.-W., Ting, C.-M.: PK-YOLO: pretrained knowledge guided YOLO for brain tumor detection in multiplane MRI slices. In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3732–3741. IEEE, Piscataway (2025)</br> ^{NOTE: MICCAI conference proceedings are part of the book series LNCS in which Springer's format for bibliographical references is strictly enforced. This is important, for instance, when citing previous MICCAI proceedings. LNCS stands for Lecture Notes in Computer Science.}

Elsevier Numbered Style: M. Kang, F.F. Ting, R.C.-W. Phan, C.-M. Ting, PK-YOLO: pretrained knowledge guided YOLO for brain tumor detection in multiplane MRI slices, in: Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3732–3741.</br> ^{NOTE: Day(s) Month Year, City, Abbrev. State, Country of Conference, Publiser, and Place of Publication are optional and omitted.}

Elsevier Name–Date (Harvard) Style: Kang, M., Ting, F.F., Phan, R.C.-W., Ting, C.-M., 2025. PK-YOLO: pretrained knowledge guided YOLO for brain tumor detection in multiplane MRI slice. In: Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 28 Februray–4 March 2025, Tucson, AZ, USA. IEEE, Piscataway, New York, USA, pp. 3732–3741.</br> ^{NOTE: Day(s) Month Year, City, Abbrev. State, Country of Conference, Publiser, and Place of Publication are optional.}

APA7: Kang, M., Ting, F.F., Phan, R.C.-W., & Ting, C.-M. (2025). PK-YOLO: Pretrained knowledge guided YOLO for brain tumor detection in multiplane MRI slice. In Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 3732–3741). IEEE. https://doi.org/10.1109/WACV61041.2025.00367

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Model Card Author

Ming Kang

Model Card Contact

[email protected]