Welcome to our comprehensive Computer Vision Bootcamp! This course is a part of our Deep Learning Bootcamp running for the year 2024. This intensive program is designed to take you from the fundamentals to advanced techniques in computer vision, equipping you with the skills to tackle real-world image processing challenges.
Our bootcamp is structured into four key sections, each building upon the previous to create a robust understanding of computer vision techniques:
We'll start by introducing PyTorch, a powerful deep learning framework. You'll learn how to manipulate tensors, build neural networks, and implement your first image classification model using Artificial Neural Networks (ANNs). To begin, please navigate to the link mentioned below. Welcome aboard!
Convolutional Neural Networks (CNNs) are a class of deep learning models widely used for image classification tasks. They are designed to automatically and adaptively learn spatial hierarchies of features from input images, making them highly effective for visual data. CNNs use convolutional layers to extract features like edges, textures, and patterns, followed by pooling layers that reduce dimensionality while retaining essential information. By stacking these layers, CNNs can learn increasingly complex representations of images, leading to accurate classification. Their ability to handle large amounts of visual data has made CNNs a cornerstone in fields like computer vision and pattern recognition. In this section, we aim to build an image classifier using CNNs.
Transfer learning involves reusing a model trained on one task, typically with large datasets, to solve a new but related task. It is particularly valuable in computer vision, where pre-trained models like ResNet or VGG are fine-tuned for applications such as object detection, image classification, and segmentation. The main advantage is that it significantly reduces the need for large amounts of labeled data and cuts down training time. By leveraging the knowledge from pre-trained models, transfer learning improves performance, especially in cases with limited data, making it a powerful approach for real-world vision problems.
Image segmentation and object detection are critical tasks in computer vision, each serving different but complementary purposes. Image segmentation involves classifying each pixel in an image, either by assigning a label to distinguish objects (semantic segmentation) or separating different instances of the same object class (instance segmentation). Object detection, on the other hand, identifies and localizes objects by drawing bounding boxes around them and labeling the detected objects. While segmentation provides more granular, pixel-level insights, object detection is effective for identifying and tracking multiple objects in a scene. Together, these tasks drive advancements in fields such as autonomous driving, medical imaging, and surveillance systems.