Step 1readingin progress360 minutes
Mathematical foundations
Review vectors, matrices, convolution, probability and optimisation concepts used throughout computer vision.
Learning path
Computer Vision Foundations
A structured path from image fundamentals and convolutional networks to detection, segmentation and modern vision transformers.
Computer Visionbeginner60 hoursin progress
0 of 10 completed0%
Path progress
Current step: Mathematical foundations
Step 2readingnot started420 minutes
Image representation and processing
Understand pixels, colour spaces, filtering, edges, features and geometric transformations.
Step 3exercisenot started480 minutes
OpenCV fundamentals
Practice loading, transforming, filtering and analysing images with OpenCV.
Step 4readingnot started600 minutes
Convolutional neural networks
Study convolution, pooling, receptive fields, training and common CNN architectures.
Step 5projectnot started720 minutes
Image classification project
Train and evaluate a CNN image classifier on a public dataset.
Step 6readingnot started480 minutes
Object detection
Study region-based and single-stage object detection methods.
Step 7readingnot started480 minutes
Image segmentation
Study semantic, instance and panoptic segmentation.
Step 8readingnot started480 minutes
Vision Transformers
Learn patch embeddings, self-attention and modern transformer-based vision architectures.
Step 9projectnot started900 minutes
Research reproduction
Choose one computer vision paper and reproduce a key experiment or result.
Step 10milestonenot started120 minutes
Path completion
Review your notes, projects and remaining research questions.