Official ASF-YOLO

This is the source code for the paper, "ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation", of which I am the first author.

Model

The model configuration (i.e., network construction) file is asf-yolo.yaml in the directory ./models/segment.

Training

The hyperparameter setting file is hyp.scratch-low.yaml in the directory ./data/hyps/.

Installation

Install requirements.txt in a Python>=3.8.0 environment, including PyTorch>=1.8.

pip install -r requirements.txt  # install

Training CLI

python segment/train.py

Testing CLI

python segment/predict.py

Evaluation

We trained and evaluated ASF-YOLO on the two datasets: the 2018 Data Science Bowl (DSB2018) from Kaggle and the Breast Cancer Cell (BCC) dataset from the Center for Bio-Image Informatics, University of California, Santa Barbara (UCSB CBI).

Suggested Citation

Our manuscript has been uploaded on arXiv. Please cite our paper if you use code from this repository:

Plain Text

• IEEE Style
  M. Kang, C.-M. Ting, F. F. Ting, and R. C.-W. Phan, "Asf-yolo: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation," arXiv:2312.06458 [cs.CV], Dec. 2023.
  

• Nature Style
  Kang, M., Ting, C.-M., Ting, F. F. & Phan, R. C.-W. ASF-YOLO: a novel YOLO model with attentional scale sequence fusion for cell instance segmentation. Preprint at https://arxiv.org/abs/2312.06458 (2023).
  

• Springer Style
  Kang, M., Ting, C.-M., Ting, F. F., Phan, R.C.-W.: ASF-YOLO: a novel YOLO model with attentional scale sequence fusion for cell instance segmentation. arXiv preprint arXiv:2312.06458 (2023)
  

License

ASF-YOLO is released under the GNU Affero General Public License v3.0 (AGPL-3.0). Please see the LICENSE file for more information.

Copyright Notice

Many utility codes of our project base on the codes of Ultralytics YOLOv5, EIoU and Soft-NMS repositories.