Customized YOLOv8 Vehicle Detection Research
Budget: ₹600 – ₹1,500 INR
I need a complete, end-to-end research project that proves real-time vehicle detection with a customised YOLOv8 model and then packages the findings in a publication-ready report.
First, you will handle the machine-learning side. Please source and preprocess a suitable open-source dataset (since I don’t have one) and train a YOLOv8 variant capable of maintaining real-time throughput. Optimisation for FPS matters more than squeezing out the very last mAP point, so feel free to experiment with model pruning, quantisation, or other tweaks as long as accuracy remains reasonable. Deliver the full Python/PyTorch codebase, the trained weights, a short demo video, and a concise README that lets me reproduce results on my own machine.
Once I confirm the model works, we move to the writing phase. I need a well-structured scientific article that reads like a novel contribution: abstract, introduction, related work, methodology, experimental setup, results, discussion, conclusion, and properly formatted references. IEEE two-column or equivalent journal layout is ideal. Highlight how the customised pipeline achieves real-time processing, include graphs or tables that support your claims, and keep language clear and academically toned.
Budget is split across two milestones: ₹1 450 released after the functional code is accepted, followed by ₹1 000 for the completed manuscript. The entire project should be wrapped up within one week, so efficient communication and incremental updates are essential.
Key deliverables
• Fully commented YOLOv8 training & inference code, trained weights, demo video, README
• Publication-ready report in editable format (Word or LaTeX PDF) with figures, citations, and proper formatting
If you’re comfortable juggling both the engineering and the writing, or can collaborate with a teammate who complements your skill set, I’m happy to discuss. Let’s create a solid, reproducible showcase of real-time vehicle detection together.
First, you will handle the machine-learning side. Please source and preprocess a suitable open-source dataset (since I don’t have one) and train a YOLOv8 variant capable of maintaining real-time throughput. Optimisation for FPS matters more than squeezing out the very last mAP point, so feel free to experiment with model pruning, quantisation, or other tweaks as long as accuracy remains reasonable. Deliver the full Python/PyTorch codebase, the trained weights, a short demo video, and a concise README that lets me reproduce results on my own machine.
Once I confirm the model works, we move to the writing phase. I need a well-structured scientific article that reads like a novel contribution: abstract, introduction, related work, methodology, experimental setup, results, discussion, conclusion, and properly formatted references. IEEE two-column or equivalent journal layout is ideal. Highlight how the customised pipeline achieves real-time processing, include graphs or tables that support your claims, and keep language clear and academically toned.
Budget is split across two milestones: ₹1 450 released after the functional code is accepted, followed by ₹1 000 for the completed manuscript. The entire project should be wrapped up within one week, so efficient communication and incremental updates are essential.
Key deliverables
• Fully commented YOLOv8 training & inference code, trained weights, demo video, README
• Publication-ready report in editable format (Word or LaTeX PDF) with figures, citations, and proper formatting
If you’re comfortable juggling both the engineering and the writing, or can collaborate with a teammate who complements your skill set, I’m happy to discuss. Let’s create a solid, reproducible showcase of real-time vehicle detection together.
Related categories:
Python
Statistics
Research Writing
SPSS Statistics
Computer Vision
Deep Learning
Scientific Writing
YOLO