Detecting bacterial flagellar motors in electron microscopy images using YOLOv8 object detection.
Why detecting bacterial motors !?
Manually identifying bacterial flagellar motors in microscopy images is time-consuming and requires expert knowledge. This automated detection system can:
- Speed up the analysis workflow for biologists
- Provide consistent detection across different images
- Help researchers process large datasets efficiently
- Serve as a foundation for more advanced detection systems
Results
- The map50 score was 87.75
- the map 50-95 score was 51.9
The model performs well but has room for improvement for higher IOU thresholds - perfect for further research and optimization!
What I Used
Model & Framework
- YOLOv8l (Large variant)
- Ultralytics 8.3.176
- PyTorch 2.6.0+cu124
Hardware
- GPU: NVIDIA Tesla T4 (15GB VRAM) [ Use collab pro for better perfomance using good gpu with more RAM]
- Platform: Google Colab
Training Configuration
- Epochs: 200(put patience level )
- Image Size: 940px
- Batch Size: 6
- Patience: 10 (early stopping)
Data Augmentation
- Rotation (±10°)
- Translation (10%)
- Scaling (20%)
- Horizontal Flip (50%)
- Mosaic Augmentation
The complete code is available in this repository. Just mount your Google Drive, upload the dataset, and you're good to go!
I'm Jayanth , I am actively working on multiple research projects and am open to collaborations in machine learning, computer vision, and biological image analysis!
If you're interested in:
- Improving this bacterial motor detection system
- Exploring novel architectures for microscopy images
- Joint research projects in ML/CV
- Publishing research papers together
- Any other research collaboration
Please feel free to reach out !!
Email: jayanth9b.vhs@gmail.com or jayanthadavi@gmail.com
I'm looking for people who want to collaborate, contribute, and build something meaningful together. Whether you're a researcher, student, or just someone passionate about ML - let's connect and workhard !