Tutorial Series

Three structured, hands-on series for mastering key Computer Vision topics. Each tutorial builds on the last with theory, code, and experiments.

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Three connected tracks

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Object Detection12 tutorials · 14–18 hrs

Self-Supervised Learning for Computer Vision5 tutorials · 12–14 hrs

Object Tracking and Multi-Object Tracking4 tutorials · 9–11 hrs

Tutorial Series Overview

Build practical computer-vision skills through guided progression

Move from frame-level object detection to reusable self-supervised representations and temporal object tracking. Every track combines conceptual foundations, algorithm flows, experiments, and implementation-focused activities.

Follow tutorials in a deliberate learning sequence

Connect mathematical ideas to runnable workflows

Compare models through controlled experiments

Finish with practical outputs and evaluation

Series

Tutorials

32–40h

Study time

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Learning tracks

All Tutorial Series

Object Detection

12 tutorials 14–18 hrs

From sliding windows to transformer-based detectors

A structured progression through object detection — from task taxonomy and evaluation metrics, through the R-CNN family, SSD and RetinaNet, YOLO architectures, transformer-based DETR, and finishing with end-to-end training, error analysis, and deployment workflows.

Prerequisites: Python & NumPy basics / CNN fundamentals / Basic OpenCV

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1Image Classification, Localization & Object Detection 2Bounding Boxes, IoU, NMS & mAP 3Sliding Window, Selective Search & R-CNN 4Faster R-CNN: Region Proposal Network 5SSD, RetinaNet & Focal Loss 6YOLO Architecture: Backbone, Neck & Head 7YOLO Training on a Custom Dataset 8OD 8A: DETR Transformer Detection 9OD 8B: RF-DETR Transformer Detection 10OD 9A: Evaluation, Error Analysis & Deployment with YOLO 11OD 9B: Evaluation, Error Analysis & Deployment with RF-DETR 12Pseudo Annotation Using SAM 3

Self-Supervised Learning for Computer Vision

5 tutorials 12–14 hrs

Reusable visual representations for classification, detection, and segmentation

Learn how unlabeled images can build transferable visual representations through contrastive learning, negative-free learning, latent-space prediction, and dense teacher guidance, then adapt those representations to a range of computer-vision tasks.

Prerequisites: Convolutional neural networks / Object-detection fundamentals / YOLO training workflow / PyTorch tensors and training loops / Image augmentation / Transfer learning / Precision, recall, IoU, and mAP

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1SimCLR Pretraining for the YOLO26 Backbone 2Negative-Free SSL with BYOL and YOLO26 3I-JEPA Predictive Pretraining for YOLO26 4DINOv3-Guided Feature Distillation into YOLO26 5Label-Efficient SSL-YOLO26 Comparative Study

Object Tracking and Multi-Object Tracking

4 tutorials 9–11 hrs

Temporal reasoning, identity preservation, and trajectory analysis

This four-part tutorial series develops the foundations of single-object and multi-object tracking. Students learn how detections are connected across time, how object states are predicted, how identities are preserved through occlusion, and how tracking systems are evaluated. YOLO26 is used as the object detector in practical demonstrations, while the tracking concepts remain applicable to any detector or tracking framework.

Prerequisites: Python / NumPy / Basic OpenCV / Bounding boxes / Intersection over Union / Object detection / Matrix multiplication / Probability fundamentals / Basic linear algebra

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1Foundations of Object Tracking and Motion Prediction 2Detection-to-Track Association: From SORT to ByteTrack 3Appearance, Occlusion, and Robust Identity Tracking 4Tracking Evaluation and Intelligent Video Analytics