COURSE PREVIEWAbout this course
Computer vision has shifted dramatically — from CNNs trained on fixed label sets to foundation models that understand images through prompts, zero-shot generalization, and multimodal reasoning. This course takes you through that entire paradigm shift, module by module, with both theory and hands-on implementation.
You'll start by understanding why attention-based architectures replaced CNNs, then go deep into the Vision Transformer (ViT) and Swin Transformer architectures — including how to fine-tune pre-trained ViTs on your own datasets. From there, you'll master end-to-end object detection with DETR and RT-DETR, learning the Hungarian matching algorithm that eliminates the need for NMS and anchors entirely.
The course then covers promptable segmentation in depth — SAM, SAM 2, and the newer SAM 3 — including point, box, and text-prompted segmentation, and zero-shot video object tracking. You'll move into multimodal vision-language models, covering contrastive pretraining (InfoNCE), VQA pipelines, and open-vocabulary detection with Grounding DINO. Finally, you'll explore label-free pretraining with DINOv2 and Masked Autoencoders (MAE), and benchmark self-supervised models against supervised baselines.
By the end, you'll be able to implement, fine-tune, and deploy today's leading vision foundation models — not just understand them conceptually. Join a supportive community of learners as you build real, practical skills you'll be proud of today.
What you'll learn
- Understand how Vision Transformers (ViT) and Swin Transformers process images via patch embeddings and attention
- Fine-tune pre-trained ViTs on custom datasets using HuggingFace Transformers
- Build end-to-end object detectors with DETR and RT-DETR, including Hungarian matching loss
- Train RT-DETR on custom datasets using Ultralytics and COCO-format annotations
- Perform zero-shot image and video segmentation with SAM, SAM 2, and SAM 3
- Implement point, box, and text-prompted segmentation pipelines
- Build multimodal pipelines combining vision encoders with LLMs for VQA and open-vocabulary detection (Grounding DINO)
- Apply self-supervised pretraining techniques (DINOv2, MAE) and compare them against supervised baselines
Before you begin
- Basic understanding of deep learning concepts (neural networks, backpropagation)
- Working knowledge of Python and PyTorch
- Familiarity with CNNs is helpful but not mandatory (covered briefly in Module 1)
- A machine with GPU access (local or cloud, e.g., Google Colab) for hands-on exercises
Who this is for
- Machine learning engineers and data scientists looking to move from classical CV (CNNs) to modern transformer-based vision models
- Computer vision practitioners who want hands-on experience with SAM, SAM 2, SAM 3, DETR, RT-DETR, and Grounding DINO
- AI engineers building multimodal applications (VQA, open-vocabulary detection, prompt-based vision pipelines)
- Students and researchers who want to understand self-supervised pretraining (DINOv2, MAE) and how it compares to supervised learning
- Not ideal for: complete beginners with no prior exposure to neural networks or Python
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