Mobilenetv2 Object Detection Fined Tuning
We are inviting AI/ML professionals, researchers, engineers, and practitioners to evaluate and provide insights on our undergraduate thesis project, Easylens.
Developed by 4th-year Computer Science students from Holy Angel University, Easylens is a lightweight real-time computer vision system designed to improve spatial awareness and navigation assistance for visually impaired individuals.
Our project focuses on:
- Data-centric preprocessing strategies
- Multi-phase transfer learning
- Real-time edge AI deployment using MobileNetV2
- Optimization for speed and accuracy on constrained devices
Current Results:
- Top-1 Accuracy: 85.55%
- Balanced Accuracy: 85.02%
- Top-2 Accuracy: 92.10%
- Inference Speed: 2.48 ms per image (400+ FPS)
We would greatly appreciate feedback regarding:
- Model architecture and training strategy
- Fine-tuning methodology
- Dataset preprocessing and augmentation
- Evaluation metrics and deployment readiness
Your insights and professional evaluation would greatly help strengthen the rigor and quality of our research.
Resources:
Evaluation Form:
https://forms.gle/kM5JJCwyZ67v7RoK9
Thank you for your time and support. We genuinely appreciate any feedback, suggestions, or observations from the AI/ML community.
