YOLOv7 Expert Needed to Review AI-Generated Object Detection Code

We are looking for a highly experienced YOLOv7 developer with at least 5+ years of hands-on experience in object detection, deep learning, and real-time computer vision applications. The ideal candidate should have expertise in training, fine-tuning, and optimizing YOLOv7 models, including dataset preprocessing, anchor box optimization, model quantization, and deployment on edge devices (TensorRT, ONNX, or OpenVINO). A strong understanding of PyTorch, image augmentation techniques, model evaluation metrics (mAP, IoU, FPS), and real-time inference acceleration is essential. Additionally, excellent English writing skills are required to provide clear, structured, and detailed feedback on AI-generated content. Prior experience with code reviews, debugging, or documentation is a plus. The task involves reviewing AI-generated prompts and responses related to YOLOv7. You will assess whether the AI-generated explanations, code snippets, and recommendations are technically accurate, efficient, and aligned with best practices in object detection and deep learning. This includes verifying code correctness, inference speed, model accuracy, and deployment feasibility. Your feedback will help improve the AI's ability to generate high-quality responses, ensuring that developers relying on these outputs receive reliable, optimized, and real-world applicable solutions.