Course Overview
- Course Title: Object Detection From Zero to Hero
- Instructor: Riad Almadani (AI & Machine Learning Engineer)
- Target Audience:
- Beginner to intermediate Python developers interested in computer vision
- Data scientists and ML engineers expanding into object detection
- Professionals seeking hands-on IceVision and fastai training
- Prerequisites:
- Basic Python programming knowledge
- Familiarity with deep learning (neural networks, training/validation loops)
- (Optional) Prior experience with fastai
Curriculum Highlights
- Key Topics Covered:
- IceVision library fundamentals (installation, configuration, workflows)
- Dataset preparation (parsers, records, custom data pipelines)
- Data augmentation with IceVision Transforms
- Model training using fastai and PyTorch Lightning
- Object detection architectures:
- Faster R-CNN
- EfficientDet
- YOLOv5
- Model evaluation (COCO mAP, precision/recall metrics)
- Deployment strategies for real-world applications
- Key Skills Learned:
- Build end-to-end object detection pipelines with IceVision
- Preprocess and augment custom datasets for computer vision
- Train and fine-tune state-of-the-art detection models
- Evaluate models using industry-standard metrics
- Deploy models in production environments
Course Format
- Duration: 2 hours of on-demand video
- Format: Self-paced online course (lifetime access)
- Resources:
- Mobile and TV access
- Certificate of completion
- (Note: No downloadable materials or quizzes explicitly listed)