Wrap Up

Congratulations on completing the course! Let’s summarize the key concepts and skills we’ve covered, ensuring we can apply our newfound knowledge to real-world computer vision projects.

In this course, we explored the main logic behind standard classification and object detection models, learning how to apply these techniques to custom-public datasets. We understood that training good models in a supercomputer is only half the battle; deploying them on edge devices is equally crucial for practical applications. We began by distinguishing between image classification and object detection, understanding that image classification involves assigning a label to an entire image, whereas object detection requires locating and classifying multiple objects within an image. We utilized Python and essential frameworks and libraries such as PyTorch for computer vision tasks, OpenCV for image processing, NumPy for mathematical operations, and Matplotlib for visualization.