Object Detection Practice Exam
Object Detection Practice Exam
Object Detection Practice Exam
The Object Detection Practice Exam is designed to assess your ability to build, train, and evaluate models that automatically identify and localize objects within images and video. This certification measures your understanding of annotation techniques, model architectures, training workflows, evaluation metrics, and deployment best practices. Whether you are a computer vision engineer, data scientist, or software developer, this exam will help you demonstrate your proficiency in object detection.
Who should take the Exam?
- Computer vision engineers and researchers
- Data scientists and machine learning practitioners
- Software developers working on image or video analysis
- AI enthusiasts and students learning deep learning
- QA engineers validating vision-based applications
Skills Required
- Basic programming in Python
- Understanding of machine learning and deep learning concepts
- Familiarity with image processing libraries (e.g., OpenCV)
- Experience with frameworks like TensorFlow or PyTorch
- Ability to work with labeled datasets
Knowledge Gained
- How to prepare and annotate datasets for object detection
- Key concepts in feature extraction and representation
- Understanding popular model architectures (e.g., Faster R-CNN, YOLO, SSD)
- Training workflows, transfer learning, and hyperparameter tuning
- Evaluation metrics such as mAP, IoU, precision, and recall
- Techniques for model optimization and real-time inference
- Deployment strategies for cloud, edge devices, and mobile
- Advanced methods like multi-scale detection and attention mechanisms
Course Outline
Domain 1 – Introduction to Computer Vision and Object Detection
- Basics of computer vision and image analysis
- Difference between classification, detection, and segmentation
- Overview of object detection applications
Domain 2 – Data Preparation and Annotation
- Collecting and curating image and video datasets
- Annotation tools and formats (COCO, Pascal VOC)
- Quality control and augmentation techniques
Domain 3 – Feature Extraction and Representation
- Handcrafted features vs. learned features
- Convolutional neural networks fundamentals
- Feature pyramids and multi-scale representations
Domain 4 – Deep Learning Models for Object Detection
- Two-stage detectors (Faster R-CNN)
- Single-stage detectors (YOLO, SSD)
- Anchor boxes and region proposal networks
Domain 5 – Training and Transfer Learning
- Setting up training pipelines
- Transfer learning and fine-tuning pre-trained models
- Hyperparameter selection and scheduling
Domain 6 – Evaluation Metrics and Testing
- Intersection over Union (IoU) and thresholding
- Mean Average Precision (mAP) calculation
- Precision-recall curves and confusion analysis
Domain 7 – Deployment and Optimization
- Model quantization and pruning
- Real-time inference on CPU, GPU, and edge devices
- Integrating models into applications and APIs
Domain 8 – Advanced Techniques and Future Trends
- Multi-scale and context-aware detection
- Attention mechanisms and transformer-based models
- Emerging applications and research directions
