About This Course
Computer vision is rapidly transforming industries — from manufacturing quality control and medical diagnostics to retail analytics and autonomous systems. The ability to build systems that can see, interpret, and act on visual data is no longer a luxury; it is a competitive necessity for organisations investing in artificial intelligence and automation.
This 10-day Computer Vision with OpenCV and PyTorch Training course offered by TrainingComplex is a comprehensive, hands-on programme designed to take participants from core image processing concepts through to the deployment of production-grade deep learning vision models. Using two of the most widely adopted tools in the field — OpenCV for classical vision tasks and PyTorch for modern neural network development — delegates build a full-stack skill set that is immediately applicable in the workplace.
Participants will explore the entire computer vision pipeline: reading and transforming images, applying classical filtering and feature extraction techniques, designing convolutional neural networks (CNNs), leveraging pre-trained models through transfer learning, and tackling advanced tasks such as object detection, semantic segmentation, and pose estimation. Each module is grounded in real industry use cases drawn from healthcare, agriculture, security, manufacturing, and media.
By the end of the programme, participants will have built and evaluated multiple vision models from scratch, fine-tuned state-of-the-art architectures, and deployed a working computer vision application — giving them the confidence and competence to lead AI vision projects within their organisations.
Who Should Attend
- Software developers and engineers seeking to specialise in computer vision and AI
- Data scientists looking to expand their expertise into image and video analytics
- Machine learning engineers transitioning from tabular to visual data workloads
- Research professionals working on AI-driven image analysis in healthcare, agriculture, or remote sensing
- IT professionals responsible for implementing AI-based surveillance or quality inspection systems
- Academics and postgraduate students working on computer vision research projects
- Technology leads and solution architects evaluating computer vision platforms for enterprise deployment
Learning Objectives
- Understand the fundamentals of digital image representation, colour spaces, and pixel-level operations
- Apply OpenCV tools for image preprocessing, filtering, morphological operations, and geometric transformations
- Extract and match key features using classical algorithms such as SIFT, ORB, and Harris corners
- Design, train, and evaluate convolutional neural networks using PyTorch from scratch
- Implement data augmentation pipelines and manage image datasets using PyTorch's DataLoader and torchvision
- Apply transfer learning and fine-tune pre-trained models including ResNet, EfficientNet, and Vision Transformers
- Build object detection systems using YOLO and Faster R-CNN architectures
- Implement semantic and instance segmentation models using DeepLab and Mask R-CNN
- Develop video processing pipelines for motion detection, optical flow, and object tracking
- Apply model optimisation techniques including quantisation, pruning, and ONNX export for deployment
- Deploy computer vision models as REST APIs and integrate them with edge devices
- Debug, profile, and interpret vision model decisions using Grad-CAM and saliency maps
- Design end-to-end computer vision solutions aligned to real organisational requirements
Course Modules
Module 1: Foundations of Digital Images and the Computer Vision Landscape
- How digital images are stored: pixels, channels, bit depth, and resolution
- Colour spaces: RGB, BGR, HSV, LAB, and when to use each
- Image file formats, compression artefacts, and their impact on vision pipelines
- Overview of the computer vision problem landscape: classification, detection, segmentation, and generation
- Setting up the development environment: Python, OpenCV, PyTorch, torchvision, and CUDA
- Reading, displaying, writing, and batch-processing images with OpenCV
Module 2: Image Preprocessing and Enhancement with OpenCV
- Geometric transformations: resizing, cropping, rotation, affine, and perspective warping
- Histogram analysis, equalisation, and CLAHE for contrast enhancement
- Smoothing and noise reduction: Gaussian, median, and bilateral filters
- Thresholding strategies: global, adaptive, and Otsu's method
- Morphological operations: erosion, dilation, opening, closing, and their applications
- Colour-based segmentation and masking for region-of-interest extraction
Module 3: Feature Detection, Description, and Matching
- Edge detection with Sobel, Laplacian, and Canny operators
- Corner detection using Harris and Shi-Tomasi algorithms
- Scale-invariant feature detection with SIFT and SURF
- Fast binary descriptors: ORB, BRIEF, and BRISK for real-time applications
- Feature matching using brute-force and FLANN-based matchers
- Homography estimation and image stitching for panorama construction
Module 4: Introduction to PyTorch for Computer Vision
- PyTorch tensors: creation, indexing, broadcasting, and device management (CPU vs GPU)
- Autograd and the computation graph: understanding backpropagation in PyTorch
- Building neural networks with torch.nn: layers, activations, and forward passes
- Defining loss functions and optimisers: SGD, Adam, and learning rate schedulers
- Writing custom Dataset and DataLoader classes for image data
- Training loops, validation loops, and checkpointing best models
Module 5: Convolutional Neural Networks: Architecture and Training
- Convolution operations: filters, stride, padding, and receptive fields explained visually
- Pooling layers, batch normalisation, and dropout for regularisation
- Building LeNet, AlexNet-style, and VGG-style networks from scratch in PyTorch
- Data augmentation with torchvision.transforms and Albumentations library
- Monitoring training with TensorBoard: loss curves, accuracy, and weight histograms
- Diagnosing overfitting and underfitting and applying corrective strategies
Module 6: Transfer Learning and Fine-Tuning Pre-Trained Models
- The transfer learning paradigm: why pre-trained weights accelerate vision tasks
- Exploring the torchvision model zoo: ResNet, VGG, MobileNet, EfficientNet, and DenseNet
- Feature extraction mode vs. full fine-tuning: when to freeze and when to unfreeze layers
- Adapting classifier heads for custom multi-class and multi-label problems
- Fine-tuning on domain-specific datasets: medical images, satellite imagery, and product photos
- Evaluating transfer learning outcomes: accuracy, F1 score, ROC-AUC, and confusion matrices
Module 7: Object Detection: Architectures and Implementation
- Object detection fundamentals: bounding boxes, anchors, IoU, and non-maximum suppression
- Two-stage detectors: understanding Faster R-CNN and its region proposal mechanism
- Single-stage detectors: YOLO architecture evolution from YOLOv3 to YOLOv8
- Training a custom object detector on a labelled dataset using PyTorch and Ultralytics
- Evaluation metrics: mean Average Precision (mAP) at various IoU thresholds
- Real-world detection pipelines for vehicle counting, defect detection, and face localisation
Module 8: Semantic and Instance Segmentation
- Pixel-wise classification: how fully convolutional networks produce segmentation maps
- Encoder-decoder architectures: U-Net design and its success in medical image segmentation
- DeepLab series: atrous convolutions, ASPP modules, and CRF post-processing
- Instance segmentation with Mask R-CNN: combining detection and per-instance masks
- Training a segmentation model on a custom annotated dataset using COCO-format labels
- Evaluating segmentation quality: mean IoU, Dice coefficient, and boundary F1 score
Module 9: Video Analysis, Optical Flow, and Object Tracking
- Reading and writing video streams with OpenCV VideoCapture and VideoWriter
- Background subtraction algorithms: MOG2 and KNN for motion detection
- Optical flow estimation: Lucas-Kanade sparse flow and Farneback dense flow
- Multi-object tracking algorithms: SORT, DeepSORT, and ByteTrack
- Action recognition fundamentals: frame-level features vs. temporal models
- Building a real-time vehicle tracking and counting pipeline from a video feed
Module 10: Vision Transformers and Advanced Architectures
- Attention mechanisms and the self-attention operation explained for vision tasks
- Vision Transformer (ViT) architecture: patch embeddings, positional encoding, and class token
- Swin Transformer: hierarchical vision processing with shifted window attention
- DETR: end-to-end object detection with transformers and bipartite matching loss
- Comparing CNN and transformer performance across classification and detection benchmarks
- Fine-tuning a pre-trained ViT on a custom image classification dataset
Module 11: Model Interpretability, Debugging, and Responsible AI
- Grad-CAM and Grad-CAM++ for visualising which image regions activate a model's decision
- Saliency maps, integrated gradients, and LIME for pixel-level attribution
- Identifying and mitigating dataset bias in vision models
- Audit frameworks for computer vision systems in sensitive domains (healthcare, surveillance)
- Confidence calibration: understanding when model confidence scores are trustworthy
- Documenting model cards and datasheets for responsible AI deployment
Module 12: Model Optimisation and Deployment
- Model performance profiling: identifying bottlenecks in inference pipelines
- Post-training quantisation (INT8) and quantisation-aware training in PyTorch
- Structured and unstructured pruning to reduce model size and latency
- Exporting models to ONNX and running inference with ONNX Runtime
- Serving vision models as REST APIs using FastAPI and Docker containers
- Edge deployment considerations: NVIDIA Jetson, OpenVINO, and TensorRT integration
Module 13: Capstone Project: End-to-End Computer Vision Solution
- Problem scoping: translating a business or research requirement into a computer vision task definition
- Dataset acquisition, annotation strategy, and preprocessing pipeline design
- Model selection, architecture customisation, and training plan development
- Experimentation tracking, hyperparameter tuning, and iterative model improvement
- Final model evaluation, interpretability analysis, and documentation of results
- Presentation of deployed solution to the group with peer and instructor feedback
Requirements & Prerequisites
Participants should have working knowledge of Python programming, including familiarity with NumPy and basic data manipulation. A foundational understanding of machine learning concepts — such as training, validation, loss functions, and gradient descent — is expected before attending. Prior exposure to PyTorch or any deep learning framework is helpful but not strictly required, as the course introduces PyTorch concepts progressively from the ground up.
Training Methodology
- Guided coding labs where participants implement every technique hands-on using Jupyter Notebooks in a pre-configured Python environment
- Instructor-led walkthroughs of real computer vision pipelines drawn from healthcare imaging, agricultural monitoring, and industrial inspection use cases
- Peer code-review sessions where participants critique and improve each other's model architectures and preprocessing pipelines
- Mini-projects at the end of each two-day block that consolidate learning through a complete build-train-evaluate cycle
- Visual debugging exercises using Grad-CAM, confusion matrices, and prediction overlays to develop model interpretation intuition
- Dataset challenges using publicly available benchmarks (COCO, ImageNet subsets, Open Images) to simulate realistic working conditions
- A capstone project in the final two days where each participant or small team designs and deploys a complete computer vision solution addressing a domain-specific problem of their choice
Organisational Benefits
- Accelerated development of in-house AI vision capabilities without heavy reliance on external vendors
- Reduced time-to-deployment for computer vision projects through trained and confident internal teams
- Improved quality control, inspection automation, and process efficiency using vision-based systems
- Stronger data science teams equipped to handle the full spectrum of structured and unstructured data
- Enhanced organisational competitiveness in AI adoption across manufacturing, health, agriculture, and security
- Better model governance through staff trained in interpretability and responsible AI for vision applications
- Cost savings from early prototyping and internal proof-of-concept development capacity
Where Is Computer Vision with OpenCV and PyTorch Training Offered?
This course runs in-person across: Kampala, Naivasha, Accra, Lagos, Cape Town, Riyadh, Kigali, Johannesburg, Dar es Salaam, Mombasa, Nairobi, Abuja, Kisumu, Addis Ababa, Arusha, Pretoria, Nakuru, Zanzibar, Dubai, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Kampala, Uganda |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 3,800 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 9,000 |
| Kigali, Rwanda |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 3,800 |
| Johannesburg, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,000 |
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,600 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Accra, Ghana |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 7,600 |
| Naivasha, Kenya |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
KES 240,000 |
| Johannesburg, South Africa |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 7,000 |
| Kampala, Uganda |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 3,800 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
KES 230,000 |
+229 more dates — view live schedule →
Frequently Asked Questions
Do I need prior experience with PyTorch or deep learning to attend this course?
A foundational understanding of machine learning concepts and Python programming is expected, but you do not need prior PyTorch experience. The course introduces PyTorch progressively, beginning with tensors and basic network construction before advancing to complex architectures.
What hardware or software do I need to participate?
TrainingComplex provides a pre-configured cloud-based GPU environment for all lab sessions, so participants do not need a high-end local machine. You will need a laptop with a modern browser and a stable internet connection. All required software, including Jupyter, OpenCV, and PyTorch, is pre-installed in the training environment.
Will I receive a certificate upon completing the course?
Yes. Participants who attend the full 10-day programme and complete the capstone project will receive a Certificate of Completion from TrainingComplex, confirming their proficiency in Computer Vision with OpenCV and PyTorch.
Is this course suitable for someone working in a non-technology industry such as agriculture or healthcare?
Absolutely. The course is deliberately designed with cross-industry case studies including agricultural monitoring, medical image analysis, and industrial inspection. Domain professionals who can programme in Python will find the content directly applicable to their sector.
How is the capstone project structured, and can I work on my own use case?
The final two days are dedicated to a guided capstone project. Participants may work individually or in small groups and are encouraged to tackle a computer vision problem relevant to their own organisation or research area. Instructors provide structured feedback throughout and during the final presentation session.
