Computer Vision | NetrAInsights

Level

Advanced

Duration

3 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

Master modern computer vision — from image classification to real-time video processing. Build end-to-end vision pipelines using PyTorch, OpenCV, and cutting-edge architectures used in autonomous vehicles, medical imaging, and AR/VR.

Image Classification: CNNs, EfficientNet, Vision Transformers
Object Detection: YOLO, Faster R-CNN, DETR
Segmentation: U-Net, Mask R-CNN, SAM
Face Recognition: FaceNet, ArcFace, MTCNN
Video Analysis: Optical flow, tracking, action recognition
3D Vision: Depth estimation, point clouds, NeRF basics

Course Modules

🖼️ Week 1: Image Classification & CNNs▼

Image representations and preprocessing
CNN architectures: VGG, ResNet, EfficientNet
Transfer learning and fine-tuning
Data augmentation strategies
Class activation maps (Grad-CAM)
Lab 1: Train image classifier from scratch
Lab 2: Fine-tune EfficientNet on custom dataset
Lab 3: Visualize learned features with Grad-CAM
Lab 4: Multi-label image classification
Lab 5: Build real-time classifier with webcam

🎯 Week 2: Detection & Segmentation▼

Region-based detection (R-CNN family)
YOLO v8 — real-time detection
DETR — detection with transformers
Semantic vs instance segmentation
SAM (Segment Anything Model)
Lab 6: Custom object detection with YOLOv8
Lab 7: Instance segmentation with Mask R-CNN
Lab 8: Medical image segmentation with U-Net
Lab 9: Zero-shot segmentation with SAM
Lab 10: Annotation pipeline with LabelImg

🎥 Week 3: Video, 3D & Deployment▼

Video preprocessing and temporal modeling
Optical flow and motion estimation
Multi-object tracking (SORT, ByteTrack)
Depth estimation with MiDaS
Exporting with ONNX, TensorRT for edge
Lab 11: Human pose estimation
Lab 12: Face recognition system
Lab 13: Multi-object tracking in video
Lab 14: Monocular depth estimation
Lab 15: Deploy model on edge with ONNX
Lab 16 (Capstone): End-to-end vision application

Prerequisites

Python (strong)
Deep Learning course or strong CNN knowledge
Linear algebra and calculus

Who Should Take This?

ML Engineers specializing in vision systems
Robotics Engineers building perception pipelines
Healthcare AI practitioners
Autonomous Vehicles engineers

Tools & Tech Stack

Python, PyTorch, torchvision, OpenCV
Ultralytics YOLOv8, Detectron2
Hugging Face (ViT, DETR, SAM)
ONNX Runtime, TensorRT
LabelImg, Roboflow

Ready to Start?

Build AI systems that can see and interpret the world with human-level precision.

📧 Enroll Now

👁️ Computer Vision