Object Detection with Python: Train and Deploy YOLO11 and Detectron2

Computer vision is transforming industries, enabling systems to automatically identify and locate objects within digital images. This text-based course guides you through the foundational concepts of deep learning for object detection using Python and PyTorch. You will transition from understanding basic neural network architectures to preparing custom datasets and training state-of-the-art models. By working through clear explanations and practical code walkthroughs, you will gain the skills to deploy these models for real-world applications. What you'll learn: - Understand the core principles of Convolutional Neural Networks (CNNs) and object detection. - Configure and train modern object detection architectures including Faster R-CNN, YOLO11, and Detectron2. - Prepare, label, and preprocess custom image datasets for deep learning pipelines. - Fine-tune pre-trained models using PyTorch to adapt them to specific detection tasks. - Export and deploy trained models using modern formats like ONNX for production environments. - Practice writing clean Python code for model inference and evaluation. The course begins with essential terminology and the mathematical foundations of computer vision before moving on to hands-on model training. You will progress systematically from basic image classification concepts to advanced multi-object detection and model deployment strategies. This course is designed for beginners in deep learning and computer vision. A basic understanding of Python programming is recommended, but no prior machine learning experience is required. Start reading today to build your first intelligent computer vision pipeline.