Computer Vision with Python: Real Projects (2026)
Master OpenCV, YOLO object detection, image classification with CNNs, and deploy CV models — 5 weeks, 4 portfolio projects, zero prior CV experience needed
Last updated: April 2026 • 13,100+ students enrolled
Key Takeaways — What you will build in 5 weeks:
- Read, manipulate, and process images/video with OpenCV — resize, crop, filter, detect edges
- Train a CNN image classifier from scratch using TensorFlow/Keras
- Use YOLO (latest version) for real-time object detection on images and webcam feeds
- Apply transfer learning — fine-tune ResNet/MobileNet on custom image datasets in hours
- Build and deploy an image classification web app with FastAPI
- Handle real CV engineering challenges — lighting variation, occlusion, class imbalance
- Complete 4 portfolio projects: Face Detector, Object Counter, Medical Image Classifier, Document Scanner
What You’ll Learn
OpenCV Image Processing
YOLO Object Detection
CNN Image Classification
Transfer Learning
Real-Time Video Analysis
Model Evaluation & Metrics
FastAPI Deployment
Edge Deployment (Basics)
Portfolio Projects You’ll Build
Face Detection App
OpenCV Haar cascades + DNN — detect and track faces in real-time from webcam
YOLO Object Counter
Count vehicles, people, or any object in video feeds using YOLOv8 + object tracking
Medical Image Classifier
Transfer learning with ResNet — classify X-ray or skin lesion images with 90%+ accuracy
Document Scanner
OpenCV perspective transform — scan any document with a phone camera, auto-straighten
Full Curriculum — 5 Weeks, 25 Lessons
Week 1 — OpenCV FundamentalsWeek 1
Lesson 1: Image basics — pixels, channels, color spaces (BGR, RGB, HSV, grayscale)
Lesson 2: Reading, writing, and displaying images with OpenCV
Lesson 3: Image transformations — resize, rotate, crop, flip, translate
Lesson 4: Filters and blurring — Gaussian, median, bilateral filters
Lesson 5: Edge and contour detection — Canny, findContours, morphological operations
Project: Document Scanner (Week 1 capstone)
Week 2 — Image Classification with CNNsWeek 2
Lesson 6: CNNs explained — convolution, pooling, activation, fully connected layers
Lesson 7: Build a CNN from scratch with Keras — dogs vs cats classifier
Lesson 8: Data augmentation — prevent overfitting with ImageDataGenerator
Lesson 9: Evaluation — accuracy, precision, recall, confusion matrix for image models
Lesson 10: Transfer learning — fine-tune MobileNetV3 on your own dataset in < 1 hour
Project: Medical Image Classifier with ResNet transfer learning
Week 3 — YOLO Object DetectionWeek 3
Lesson 11: Object detection fundamentals — bounding boxes, IoU, mAP
Lesson 12: YOLOv8 setup — run inference on images and video in 5 minutes
Lesson 13: Custom YOLO training — annotate your own dataset with Roboflow
Lesson 14: Object tracking — ByteTrack and DeepSORT for tracking objects across frames
Project: YOLO Object Counter — count objects in real-time video
Week 4 — Face Recognition & Real-Time CVWeek 4
Lesson 15: Face detection — Haar cascades, DNN face detector, MediaPipe
Lesson 16: Face recognition — face embeddings, similarity matching with face_recognition library
Lesson 17: Pose estimation with MediaPipe — detect body keypoints in real-time
Lesson 18: Optical flow — motion detection and tracking
Project: Real-Time Face Detection App with webcam
Week 5 — Model Deployment & Production CVWeek 5
Lesson 19: Model optimization — ONNX export, quantization for faster inference
Lesson 20: FastAPI for CV — build a /classify image REST endpoint
Lesson 21: Containerizing CV apps — Dockerfile for OpenCV + TensorFlow
Lesson 22: Edge deployment basics — ONNX Runtime on Raspberry Pi/Jetson
Lesson 23: Production CV patterns — batching, async inference, image preprocessing pipelines
Prerequisites
- Python programming — NumPy and basic array operations are especially helpful
- Basic math — matrix operations and derivatives (explained from scratch in the course)
- A computer with 8GB+ RAM; GPU optional but helpful (Google Colab free GPU is fine)
- No prior CV or deep learning experience needed
Career Outcomes & Salaries
Computer Vision Engineer
₹10–22 LPA
Build CV systems for manufacturing inspection, retail analytics, security, and healthcare imaging
AI QA Engineer
₹8–16 LPA
Automated visual inspection using CV — replace manual QA in manufacturing with AI-powered defect detection
CV Research Engineer
₹18–40 LPA
Work on state-of-the-art CV research at product companies, automotive AI labs, and healthcare tech
ML Engineer (Vision)
₹12–28 LPA
Full-stack ML engineering for vision products — training, evaluation, optimization, and deployment
What Students Say
★★★★★
“The YOLO section is hands-down the best practical CV content I’ve found online. I trained a custom model to detect safety equipment on a construction site for my final year project. Got an A+.”
Tejas Kulkarni
Final Year B.Tech, VJTI Mumbai
★★★★★
“Medical image classifier in Week 2 was incredible. I used transfer learning with ResNet to classify diabetic retinopathy and got 94% accuracy. This project got me an internship interview at a health-tech startup.”
Ishaan Mehta
M.Tech AI Student, IIT Bombay
★★★★☆
“The deployment week is what sets this apart from other CV courses. I can now deploy a model as an API. Before this course, my models only worked on my laptop.”
Poonam Wagh
ML Engineer, Tata Consultancy Services AI
Frequently Asked Questions
How do I start learning computer vision as a beginner with Python?
Start with OpenCV basics — image reading, transformations, filtering — then move to CNNs for classification, then YOLO for detection. This course follows exactly this 5-week progression. All you need to start is Python basics and curiosity.
What is YOLO and how does object detection work?
YOLO (You Only Look Once) processes an image in a single neural network pass and simultaneously predicts object classes, bounding box locations, and confidence scores. YOLOv8/YOLO11 detect 80+ object classes in real-time. In Week 3, you’ll use YOLO on live webcam feeds and train it on custom datasets.
What computer vision projects can I add to my resume?
This course builds 4 portfolio projects: Face Detection App, YOLO Object Counter, Medical Image Classifier, and Document Scanner. These cover the main CV domains — surveillance, counting, healthcare, and document processing — and demonstrate both classical CV and deep learning skills.
What is the salary of a Computer Vision Engineer in India in 2026?
Entry-level: ₹8–15 LPA. Mid-level (3–5 years): ₹18–35 LPA. Senior CV Engineers at automotive AI, healthcare imaging, and surveillance tech earn ₹35–70 LPA. CV is one of the most specialized and premium AI disciplines.
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