Mastering Computer Vision: From Pixel to Detection to Gen-CV
The Complete Computer Vision Blueprint: Master OpenCV, CNNs, ResNet, YOLOv8-v11, RT-DETR, U-Net, SAM 2 (Segment Anything), Vision Transformers, and Generative CV.
Two Flexible Ways to Master Computer Vision
Choose between self-paced on-demand videos on Udemy or live weekly cohort mentorship with real-time edge CV labs.
Buy Directly on Udemy
Learn at your own pace with lifetime on-demand access to all 34+ hours of video, 120+ code lessons, and downloadable repos.
- 34+ hours on-demand video & 120+ code walkthroughs
- Real-time object detection: YOLOv8/v10/v11 & RT-DETR
- Zero-shot video tracking with Segment Anything (SAM 2)
- NVIDIA TensorRT acceleration (>120 FPS) & certificate
Join the Live Cohort
Weekly live Zoom architecture workshops with Vinit Singh, 1-on-1 office hours, private Discord vision lab, and custom edge CV reviews.
- Includes everything in the On-Demand course +
- 6 weeks of live interactive Zoom coding sessions & Q&A
- 1-on-1 weekly instructor office hours & TensorRT profiling
- Private Discord peer cohort & edge hardware lab reviews
- Official gadgap AI Certified Computer Vision Engineer Credential
Course Description & Overview
Whether you are processing raw camera pixel feeds with OpenCV or deploying real-time multi-camera tracking systems to edge devices, this bestseller masterclass covers the entire computer vision spectrum from classical image processing to the latest foundation models.
You will build real-world vision applications across classical image filtering, deep convolutional neural networks (ResNet, ConvNeXt), state-of-the-art real-time detectors (YOLOv8, YOLOv10, YOLOv11, RT-DETR), transformer-based zero-shot segmentation with Meta's Segment Anything (SAM & SAM 2), and edge inference optimizations using NVIDIA TensorRT and ONNX Runtime.
Course Requirements & Prerequisites
- Basic Python: Basic Python programming knowledge (loops, functions, lists).
- Mathematics: Basic high-school math (coordinate grids, matrices, vectors).
- Setup: Any laptop or desktop capable of running Python and OpenCV.
Who This Course Is For (Intended Learners)
Designed to take learners from foundational pixels to advanced spatial intelligence.
Software Engineers Transitioning into AI
Developers wanting a rigorous, code-first introduction to computer vision, camera stream processing, and deep learning backbones.
Robotics & Embedded Edge Developers
Engineers building autonomous drones, robotics vision systems, and edge IoT devices running accelerated YOLO and TensorRT models.
Surveillance & Industrial Inspection Pros
Specialists implementing automated defect detection, retail traffic heatmaps, license plate recognition, and security anomaly detection.
Students & Data Science Practitioners
Learners looking to expand from tabular data into multi-modal spatial computing with a standout portfolio of production projects.
Comprehensive 6-Module Curriculum
- Image representations: RGB, BGR, HSV, LAB, and grayscale matrix operations
- Spatial filtering: Gaussian blur, Sobel, Laplacian, and Canny edge detection
- Morphological operations: Dilation, erosion, opening, closing, and hit-or-miss
- Geometric transformations, perspective warp, and camera calibration
- Convolution layers, pooling, stride, padding, and receptive fields
- Modern backbones: ResNet, ConvNeXt, MobileNetV4, and EfficientNet
- Vision Transformers (ViT): Patch embeddings, self-attention, and spatial positional encoding
- Transfer learning, data augmentation (Albumentations), and learning rate scheduling
- Anchor-based vs Anchor-free detectors: YOLO architecture family (YOLOv8 to YOLOv11)
- Real-Time DEtection TRansformer (RT-DETR) and Hungarian matching loss
- Loss functions: CIoU, DIoU, GIoU, and focal loss for class imbalance
- Multi-object tracking: DeepSORT, ByteTrack, and BoT-SORT for video streams
- Semantic vs Instance vs Panoptic segmentation architectures (U-Net, Mask R-CNN)
- Segment Anything Model (SAM) and SAM 2 for video object segmentation
- Promptable visual segmentation: points, bounding boxes, and natural language masks
- Zero-shot object localization with Grounding DINO + SAM pipeline
- Model export to ONNX format, graph simplification, and shape dynamic axes
- NVIDIA TensorRT acceleration: FP16 and INT8 Post-Training Quantization (PTQ)
- Multi-threaded video decoding with NVIDIA DeepStream and OpenCV GStreamer pipelines
- Benchmarking throughput (FPS) vs latency across CPU, GPU, and Jetson edge devices
- Vision-Language Models (VLMs): CLIP, LLaVA, and Qwen2-VL for zero-shot image understanding
- Synthetic data generation using Diffusion models for training CV detectors
- Combining CV detection boxes with Generative inpainting for privacy redaction
- Capstone: Deploy an end-to-end intelligent store analytics system with anomaly alerts
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
Buy on Udemy for on-demand self-paced access, or join our live interactive cohort for hands-on mentorship.