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Computer Vision Engineering

From convolutions to ViTs. Build models that see

6 phases. 28 lessons. 28 labs. 1 capstone. Computer vision from convolutions to cutting-edge — CNNs from scratch (convolution, pooling, architectures), object detection (YOLO, DETR, Faster R-CNN), segmentation (U-Net, Mask R-CNN, SAM), and modern vision (Vision Transformers, CLIP, DINOv2, diffusion for vision). You build a vision model that detects, segments, and classifies objects in real images.

6 phases · 28 lessons · 28 labs · 1 capstone

Take Deep Learning from Scratch first — this course builds on it.

Outcomes you will have by the end

• A complete vision system — A vision pipeline that classifies, detects, and segments objects in real images — combining CNNs, ViTs, and modern architectures into a unified system.
• A working mental model of how vision models see — Convolutions, feature maps, anchor boxes, attention over image patches — you built each component from scratch. When a vision model misclassifies, you know whether it's the data, the architecture, or the training.
• Experience with the full CV toolkit — From classical convolutions to YOLO to ViTs to CLIP to diffusion — you've used the entire spectrum of computer vision techniques, not just one framework.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the CNN, detection, segmentation, and modern vision skills you built.

What you will be able to do

2D Convolutions from Scratch · CNN Architectures (LeNet to ResNet) · Transfer Learning for Vision · Object Detection (YOLO, Faster R-CNN, DETR) · Semantic & Instance Segmentation (U-Net, Mask R-CNN, SAM) · Vision Transformers (ViT) · CLIP & Image-Text Matching · Diffusion Models for Image Generation

Every phase, every lesson, every project

• Vision Foundations (5 lessons) — free — Image processing, convolutions, and edge detection from scratch.
  • Image Fundamentals — Pixels, Channels, Color Spaces
  • Convolutions from Scratch
  • CNNs — LeNet to ResNet
  • Image Classification
  • Transfer Learning & Fine-Tuning
• Convolutional Neural Networks (5 lessons) — CNNs, pooling, and classic architectures from scratch.
  • Object Detection — YOLO from Scratch
  • Semantic Segmentation — U-Net
  • Instance Segmentation — Mask R-CNN
  • Image Generation — GANs
  • Image Generation — Diffusion Models
• Object Detection & Segmentation (5 lessons) — YOLO, R-CNN, and segmentation from scratch.
  • Stable Diffusion — Architecture & Fine-Tuning
  • Video Understanding — Temporal Modeling
  • 3D Vision — Point Clouds & NeRFs
  • Vision Transformers (ViT)
  • Real-Time Vision — Edge Deployment
• Generative Vision Models (5 lessons) — Autoencoders, GANs, and diffusion models for images.
  • Build a Complete Vision Pipeline — Capstone
  • Self-Supervised Vision — SimCLR, DINO, MAE
  • Open-Vocabulary Vision — CLIP
  • OCR & Document Understanding
  • Image Retrieval & Metric Learning
• Vision Transformers (5 lessons) — ViTs, CLIP, and multimodal vision models.
  • Keypoint Detection & Pose Estimation
  • 3D Gaussian Splatting from Scratch
  • Diffusion Transformers & Rectified Flow
  • SAM 3 & Open-Vocabulary Segmentation
  • Vision-Language Models — The ViT-MLP-LLM Pattern
• CV Deployment & Applications (3 lessons) — Model optimization, deployment, and real-world CV applications.
  • Monocular Depth & Geometry Estimation
  • Multi-Object Tracking & Video Memory
  • World Models & Video Diffusion

The technologies you will use

PyTorch · OpenCV · torchvision · Hugging Face Transformers · Albumentations

Roles this course prepares you for

• Computer Vision Engineer ($120k–$180k) — Build vision systems for real-world applications — surveillance, medical imaging, autonomous vehicles, quality inspection. Understanding CNNs and ViTs from scratch means you can choose the right architecture and debug detection quality issues.
• Medical Imaging Engineer ($115k–$170k) — Build vision models for medical diagnosis — tumor detection, organ segmentation, X-ray analysis. U-Net and segmentation expertise are essential for this role.
• Autonomous Systems Engineer ($130k–$200k) — Build perception systems for autonomous vehicles and robots — real-time object detection, lane segmentation, depth estimation. YOLO and real-time inference expertise are critical.

What computer vision engineering actually is

It's building models that understand visual data — detecting objects, segmenting regions, classifying scenes, and generating images. From classical convolutions to modern Vision Transformers, you implement the full spectrum of CV techniques and build systems that see.

What you do every day

You design vision architectures, prepare image data pipelines, and train models for real-world visual tasks. When a detection model misses objects or a segmentation model produces poor masks, you diagnose the issue from first principles — because you built the components yourself.

Why companies hire for this

Computer vision powers autonomous vehicles, medical imaging, surveillance, quality control, and augmented reality. Companies need engineers who can build and deploy vision systems, not just call pre-trained APIs. The engineer who built YOLO and U-Net from scratch is the one who can customize them for novel applications.

What this course is not

It is not an OpenCV tutorial. It is not about image processing filters. It is about building neural networks that understand images — from convolutions to Vision Transformers to diffusion models. If you want to build vision systems that learn, this is the course.

Common questions

Do I need Deep Learning from Scratch first?

Yes — this course uses PyTorch, CNNs, and training loops throughout. Deep Learning from Scratch builds those foundations; this course applies them specifically to vision data.

Do I need a GPU?

For the first two phases, no — you can train small CNNs on CPU. For phases 3–4 (detection, segmentation, ViTs), a GPU is strongly recommended. The course includes guidance on using free GPU resources (Google Colab, Kaggle).

How is this different from the CV phase in ML & AI Engineering?

ML & AI Engineering covers CV in 5 lessons — enough to use pre-trained models. This course spends 16 lessons implementing convolutions from scratch, building YOLO and U-Net, and covering modern architectures like ViTs, CLIP, and diffusion. It's the difference between using a vision model and building one.

What do I end up with?

A complete vision system that classifies, detects, and segments objects in real images — plus the ability to implement any CV architecture from a research paper.

Continue your learning path

Deep Learning from Scratch · Multimodal AI Systems · Generative AI Fundamentals · ML & AI Engineering

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