From OpenCV to Object Detection to the Edge of Generative Vision
Computer vision from pixels to production — image processing, CNNs, detection, segmentation, and the exact three concepts (VAEs, GANs, ViT) the Generative Vision & Video course assumes you already have.
What You Will Master & Deploy
Gain the complete engineering spectrum from classical pixels to deep detection and transformer vision.
3 Generations of Object Detection
Build a real object detection pipeline across three architectural generations — two-stage (Faster R-CNN), single-stage (YOLO, SSD), and transformer-based (DETR).
Semantic & SAM Segmentation
Implement both semantic and instance segmentation, from classical U-Net and Mask R-CNN to Meta's state-of-the-art Segment Anything Model (SAM).
Generative On-Ramp (VAEs, GANs, ViT)
Get a working foundation in VAEs, GANs, and Vision Transformers — the exact three prerequisites that the advanced Generative Vision & Video course assumes you already have.
Is This Course Right For You?
Verify whether this course matches your current goals and skill level.
This is for you if:
- You're new to computer vision and want to build real detection and segmentation systems, not just call a vision API.
- You're targeting production CV applications (automated quality control, retail inventory, industrial inspection, robotics).
- You're planning to take Generative Vision & Video next and want its Module 0 to be reinforcement, not brand-new material.
This is NOT for you if:
- You've already covered VAEs, GANs, and Vision Transformers (this course's Module 6) — skip straight to Mastering Generative Vision & Video, which picks up exactly where Module 6 leaves off.
- You want only creative/generative image work with no interest in detection, segmentation, or classical OpenCV.
Enroll in Computer Vision
Master computer vision from OpenCV pixel operations through modern transformers.
Enroll Directly on Udemy
Learn at your own pace with lifetime access to all 20+ hours of video lessons, code repositories, and detector weights.
- Classical image processing & filtering in OpenCV
- CNN architectures & transfer learning (ResNet, EfficientNet)
- Object detection across 3 generations (YOLO, Faster R-CNN, DETR)
- Semantic segmentation (U-Net) and Meta's SAM foundation model
Comprehensive 6-Module Curriculum
From image fundamentals to object detection, segmentation, and generative foundations.
- Digital image representations, pixels, color spaces (RGB, HSV, Grayscale)
- OpenCV matrix operations and coordinate systems
- Spatial filtering, convolution kernels, Gaussian blur, and Sobel edge detection
- Geometric transformations, thresholding, and morphological operations
- Perceptrons to multilayer networks: Activation functions and backpropagation
- Convolutional layers, pooling, receptive fields, and feature maps
- Building and training your first end-to-end CNN in PyTorch/TensorFlow
- Loss functions, optimizers (AdamW, SGD), and preventing overfitting
- Evolution of deep vision: VGG, ResNet (residual connections), Inception, EfficientNet
- Transfer learning strategies: Feature extraction vs full fine-tuning
- Data augmentation pipelines (Albumentations, CutMix, MixUp)
- Model calibration, confusion matrices, and precision-recall trade-offs
- Two-stage detectors: R-CNN, Fast R-CNN, Faster R-CNN, Region Proposal Networks (RPN)
- Single-stage detectors: YOLO (v8/v11), SSD architectures, anchor boxes, and NMS
- Transformer-based detection: DEtection TRansformer (DETR) and bipartite matching loss
- Evaluation metrics: Intersection over Union (IoU) and mean Average Precision (mAP@50-95)
- Semantic vs Instance vs Panoptic segmentation differences
- U-Net architecture: Encoder-decoder pathways and skip connections
- Mask R-CNN for bounding box + pixel-level mask prediction
- Foundation models for vision: Meta's Segment Anything Model (SAM) promptable segmentation
- Variational Autoencoders (VAEs): Continuous latent representations and reconstruction loss
- Generative Adversarial Networks (GANs): Minimax game, generator-discriminator dynamics
- Vision Transformers (ViT): Patch extraction, self-attention, and spatial positional embeddings
- Connecting discriminative vision to generative diffusion foundations
Computer Vision FAQs
How is this course different from Mastering Generative Vision & Video?
Mastering Computer Vision covers discriminative understanding — detection, classification, pixel-level segmentation, OpenCV, and edge models. Mastering Generative Vision & Video is generative — synthesizing images, video, and audio-visual sync from scratch. This course's Module 6 serves as the direct on-ramp to Gen-CV.
What hardware do I need to follow along?
A standard laptop is sufficient for OpenCV and basic CNN labs. For object detection and transformer training, free Google Colab or Kaggle GPUs are completely adequate.
Do we cover modern architectures like YOLOv11 and Meta SAM?
Yes. You will learn both historical foundational models (Faster R-CNN, U-Net) and modern architectures including real-time YOLO variants, DETR transformers, and Meta's Segment Anything Model (SAM).
Vinit Singh
AI Consultant & Principal AI Architect, gadgap AI
Vinit is an AI Consultant and Educator specializing in LLM Fine-Tuning, Voice AI, Speech Language Models, Agentic AI, and Computer Vision — with over 18 years of experience in Data Science and Artificial Intelligence. A graduate of IIT Bombay with Stanford Machine Learning and Deep Learning certifications, he works at the intersection of AI research and real-world deployment.
Currently a Consultant in the Speech & Language team at Sony India Software Centre, his prior work spans Computer Vision on Nvidia edge hardware at Assert AI, and end-to-end AI consulting at tvam Technologies — where he built agentic FinTech workflows, a robo-advisor via LoRA fine-tuning on DeepSeek-R1, and a Voice AI telecaller pipeline.
A top 3% Udemy creator globally, trusted by learners across 150+ countries and enterprises including Adidas, Barclays, and Volkswagen — covering Voice AI, Agentic AI, Computer Vision, and NLP & LLMs.
Enroll in Computer Vision
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