Computer Vision Concepts

Image fundamentals through CNNs, object detection, segmentation, generative models, vision transformers, and 3D vision.

Start Module 01

Curriculum

A structured path through the course content.

🖼

Module 01 9 concepts Start here

Image Fundamentals

Pixels, color spaces, image formats, and basic operations.

🔍

Module 02 10 concepts

Feature Extraction & Classical Vision

Edge detection, feature descriptors, and classical CV algorithms.

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Module 03 12 concepts

Convolutional Neural Networks

CNN architectures, convolution operations, and design principles.

📈

Module 04 10 concepts

Training & Optimization

Data augmentation, transfer learning, and training strategies.

🎯

Module 05 12 concepts

Object Detection

YOLO, R-CNN families, anchor-based and anchor-free methods.

✂

Module 06 9 concepts

Image Segmentation

Semantic, instance, and panoptic segmentation.

🎨

Module 07 10 concepts

Generative Models

GANs, diffusion models, and image generation.

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Module 08 8 concepts

Vision Transformers

ViT, DeiT, and attention-based vision architectures.

🎬

Module 09 8 concepts

Video Understanding

Temporal modeling, action recognition, and video analysis.

🧊

Module 10 9 concepts

3D Vision

Depth estimation, point clouds, and 3D reconstruction.

🌐

Module 11 9 concepts

Multimodal & Foundation Models

CLIP, vision-language models, and foundation models for vision.

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Module 12 8 concepts

Applications & Deployment

Edge deployment, optimization, and real-world applications.

📊

Module 13 6 concepts

Evaluation & Datasets

Benchmarks, metrics, and standard datasets.