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Official implementation of "BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection".

License: GNU Affero General Public License v3.0

Python 99.47% Shell 0.53%
afpn bi-level-routing-attention biformer bifpn br35h-brain-tumor-detection-2020 cbam ciou-loss computer-vision-algorithms deep-learning-framework deep-neural-networks

bgf-yolo's Introduction

Official BGF-YOLO

This is the source code for the paper, "BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection", of which I am the first author.

Model

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

The hyperparameter setting file is default.yaml in the directory ./yolo/cfg/.

Installation

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

pip install -r requirements.txt  # install
Training CLI
python yolo/bgf/detect/train.py

Testing CLI

python yolo/bgf/detect/predict.py

Evaluation

We trained and evaluated BGF-YOLO on the dataset Br35H :: Brain Tumor Detection 2020. The .txt format annotations in the file dataset-Br35H.zip are coverted from original json format.

Model Precision Recall mAP50 mAP50:95
RT-DETR-X 0.825 0.770 0.870 0.597
Co-DETR with Swin L (36 Epochs, DETR Augmentation) โ€“ โ€“ 0.941 0.609
YOLOv9-E 0.927 0.869 0.919 0.630
BGF-YOLO (Ours) 0.919 0.926 0.974 0.653

Generalizability in External Validation

We conducted additional experimental validation on a different domain using the COVID-19 facemask detection dataset. The table below consistently shows the superior detection performance of our method compared to YOLOv8x. This indicates the generalizability of our method to other domains of object detection.

Model Precision Recall mAP50 mAP50:95
YOLOv8x 0.866 0.773 0.802 0.494
BGF-YOLO (Ours) 0.847 0.764 0.820 0.504

License

BGF-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 YOLOv8, GiraffeDet, DAMO-YOLO, and BiFormer repositories.

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