YOLO Vehicle Detector Training
Budget: $10 – $30 AUD
I need a robust YOLO model that spots vehicles with high accuracy. Because I do not yet have a labeled dataset, the job begins with sourcing or capturing varied vehicle images and annotating them in classic YOLO format. Once the dataset is in place, the next step is to train and validate the network—YOLOv5 or YOLOv8 are both fine as long as the final mAP holds up under real-world conditions. Please apply best-practice augmentation, tune hyper-parameters, and track training with clear metrics so I can reproduce the results later in PyTorch.
Deliverables
• Curated and fully annotated vehicle image dataset (bounding-box labels in YOLO txt format)
• Trained YOLO weights and configuration files
• Short report summarising dataset composition, training settings, and evaluation scores
• Simple Python inference script (or notebook) that loads the model and runs detection on sample images
Acceptance criteria
– mAP ≥ 0.85 on an unseen validation split
– No licensing restrictions on the supplied imagery
– All files organised in a clean repository structure ready for deployment
Deliverables
• Curated and fully annotated vehicle image dataset (bounding-box labels in YOLO txt format)
• Trained YOLO weights and configuration files
• Short report summarising dataset composition, training settings, and evaluation scores
• Simple Python inference script (or notebook) that loads the model and runs detection on sample images
Acceptance criteria
– mAP ≥ 0.85 on an unseen validation split
– No licensing restrictions on the supplied imagery
– All files organised in a clean repository structure ready for deployment