Course Details
Advanced Computer Vision
Deep dive into object detection, image segmentation, and real-time video analytics using state-of-the-art architectures.
Hands-on Highlights
This program is designed around shipping functional, production-ready AI applications from day one. You will move beyond theory and build real products.
- 1Train YOLOv9 for custom object detection
- 2Implement real-time pose estimation algorithms
- 3Build an automated defect detection system
- 4Deploy CV models using NVIDIA TensorRT
Detailed Syllabus
Modern CNN Architectures
- →Deep dive into ResNet, ConvNeXt, and MobileNet
- →Advanced image augmentation pipelines
- →Loss functions for classification
- →Hyperparameter tuning for vision models
Object Detection (YOLO)
- →Understanding bounding boxes, IoU, and mAP
- →The YOLO family architecture (YOLOv8/v9)
- →Dataset annotation and preparation
- →Training custom object detectors
Image Segmentation & Pose
- →Semantic vs Instance Segmentation (Mask R-CNN)
- →U-Net for medical and industrial applications
- →Human pose estimation algorithms
- →Real-time video tracking (DeepSORT)
Edge Deployment & TensorRT
- →Model quantization (INT8, FP16)
- →Exporting models to ONNX
- →Optimizing inference with NVIDIA TensorRT
- →Deploying on Jetson Nano
Format & Location
Hybrid Learning
Choose between intensive IRL bootcamps at our physical campuses in Pondicherry, Vellore, or Coimbatore, or join our flexible online programs designed for working professionals.
Prerequisites
Who is this for?
We assume a fundamental understanding of Python and basic programming concepts. This is for developers looking to master deep learning and build real AI products.
Elite Mentorship
Learn from builders
Learn directly from engineers who have built and scaled AI systems. Get code reviews, architecture feedback, and career guidance.
Outcomes
Ship real products
Our primary focus is helping you build a strong portfolio of real, deployed AI products to show employers. All graduates receive a verifiable certificate.
Ready to build?
Join our next cohort to get started.