Module #12 Object Detection Datasets Popular datasets:Pascal VOC, COCO, KITTI, and Open Images
Module #13 Object Recognition Techniques Overview of object recognition, importance, and applications
Module #14 Features Extraction for Object Recognition Types of features:SIFT, SURF, ORB, and Bag-of-Words
Module #15 Classification-based Object Recognition Using classification models:SVM, k-NN, and Random Forest
Module #16 Deep Learning-based Object Recognition Using CNNs and transfer learning for object recognition
Module #17 Object Recognition using Embeddings Using feature embeddings:triplet loss, siamese networks, and metric learning
Module #18 Object Recognition Challenges and Limitations Handling variations:occlusion, pose, lighting, and viewpoint
Module #19 Real-World Applications of Object Detection and Recognition Case studies:self-driving cars, surveillance, medical imaging, and robotics
Module #20 Object Detection and Recognition in Videos Tracking objects across frames, motion detection, and video analysis
Module #21 Object Detection and Recognition using 3D Data Using LiDAR, stereo vision, and 3D point clouds for object detection
Module #22 Object Detection and Recognition in Edge Computing Optimizing models for edge devices, model compression, and quantization
Module #23 Object Detection and Recognition in Cloud Computing Scaling models for cloud infrastructure, distributed training, and data parallelism
Module #24 Object Detection and Recognition using Transfer Learning Fine-tuning pre-trained models for specific object detection tasks
Module #25 Object Detection and Recognition using Domain Adaptation Adapting models to new domains, datasets, and environments
Module #26 Object Detection and Recognition using Ensembles Combining multiple models for improved performance and robustness
Module #27 Object Detection and Recognition using Explainability Techniques Using saliency maps, Grad-CAM, and feature importance for model interpretability
Module #28 Object Detection and Recognition Project Development Guided project development:selecting datasets, designing models, and evaluating performance
Module #29 Object Detection and Recognition Best Practices Tips and tricks for training, tuning, and deploying object detection and recognition models
Module #30 Course Wrap-Up & Conclusion Planning next steps in Object Detection and Recognition Techniques career
