To learn computer vision effectively, start with foundational projects that introduce core concepts. Begin by implementing image classification using pre-trained models like ResNet or MobileNet with frameworks such as TensorFlow or PyTorch. For example, build a simple application that distinguishes between cats and dogs using a dataset like Kaggle’s Dogs vs. Cats. This teaches you how to load data, preprocess images (resizing, normalization), and use transfer learning to adapt a model for a specific task. You’ll also learn to evaluate performance using metrics like accuracy and confusion matrices. Extend this by experimenting with data augmentation techniques (rotations, flips) to improve generalization, which is critical for handling real-world variability.
Next, tackle object detection and segmentation to explore localization tasks. Use OpenCV and libraries like Detectron2 or YOLO (You Only Look Once) to build a system that identifies and outlines objects in images or video streams. For instance, create a pedestrian detector for traffic camera footage. This requires understanding bounding boxes, anchor boxes, and non-max suppression to filter overlapping predictions. For segmentation, try Mask R-CNN to differentiate objects pixel-by-pixel, useful in medical imaging or autonomous vehicles. These projects introduce annotation formats (COCO, Pascal VOC), model architectures for spatial reasoning, and tools like LabelImg for creating custom datasets. You’ll also learn to handle video input by processing frames sequentially and optimizing inference speed.
Finally, explore advanced applications like real-time gesture recognition or 3D reconstruction. Use MediaPipe or OpenPose to track hand movements and map them to commands, such as controlling a virtual keyboard. This involves working with keypoint detection and temporal consistency across video frames. For 3D tasks, experiment with Structure-from-Motion (SfM) using libraries like OpenMVG to generate 3D models from 2D images. Another project could involve stereo vision with dual cameras to estimate depth, similar to how self-driving cars perceive distance. These projects demand integrating multiple techniques (feature matching, epipolar geometry) and optimizing latency for real-time use. By progressively increasing complexity, you’ll build a robust understanding of both theory and practical implementation in computer vision.