Udemy
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Mastering Vision AI: From Models to Prompts to Multimodal
Hot & New
New
Rating: 5.0 out of 5(8 ratings)
115 students

Mastering Vision AI: From Models to Prompts to Multimodal

Master ViT, DETR, SAM, SAM 2, Grounding DINO & DINOv2 — from architecture to real-world fine-tuning
Created byVinit Singh
Last updated 10/2026
English

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

Course content

6 sections • 294 lectures • 25h 0m total length

  • Introduction - MODULE 1: Foundation Review & Modern Paradigm Shift3:41
  • Intro Lect 1.1 From CNNs to Transformers: The Attention Paradigm Shift2:26
  • 1111 Vision Transformers (ViT) - The Paradigm Shift6:14
  • 1112 The Vision Transformer ( ViT) Architecture7:58
  • 1113 Why Vision Needs a Global View8:18
  • 1114 CNN vs. ViT A Tale of Two Perspectives3:22
  • Code Eg Vision Transformers: The Attention Paradigm Shift4:43
  • Intro Lect 1.2 Why Foundation Models Matter: ImageNet → Zero-Shot Era3:38
  • 1211 Why Foundation Models Matter – ImageNet → Zero‑Shot Era8:13
  • 1212 Theoretical Foundations – From Fixed Labels to Infinite Manifolds8:36
  • 1221 Key Insight & Core Concepts – The Fou5:12
  • 1222 Detailed Working Theory – Step 1 Uncurated Data Ingestion2:58
  • 1223 Step 3 Inference via Latent Similarity2:03
  • 1231 Key Models – The ImageNet Era Deep CNNs (e.g., ResNet7:35
  • 1232 The Foundation Era – Vision Transformers & Contrastive Models (e.g., CLIP)3:22
  • 1233 How CLIP Achieves Zero‑Shot Transfer2:00
  • 1241 Self-Supervised Foundations – MAE & DINO5:20
  • 1242 DINO – Self-Distillation with No Labels4:15
  • 1243 Theoretical Challenges & Open Problems – Foundation Models8:33
  • 1251 Evolution and Historical Context – From ImageNet to Zero-Shot Foundations5:51
  • 1252 Future Theoretical Directions – From Correlation to Causation and World Mod7:17
  • Code Eg 1.2 Why Foundation Models Matter: ImageNet → Zero-Shot Era7:23
  • Intro Lect 1.3 The Modern Vision Stack: Scaling, Pretraining, Prompt-Based Infer2:30
  • 1311 The Modern Vision Stack – Scaling, Pretraining, Prompt-Based Inference6:46
  • 1312 Theoretical Foundations – The Pillars of the Modern Vision Stack8:00
  • 1313 Core Concepts – Scaling The Hypothesis Space Expansion5:33
  • 1321 Pretraining – Contrastive & Generative Objectives7:26
  • 1322 Prompt-Based Inference – Conditional Probability & The Prior Effect5:01
  • 1323 Detailed Working Theory – The Lifecycle of a Foundation Model4:45
  • 1331 Key Models – Segment Anything Model (SAM)4:04
  • 1332 Zero‑Shot Segmentation with SAM5:09
  • 1333 Theoretical Significance – Prompting Works for Geometry Too10:39
  • 1334 Theoretical Challenges & Open Problems – The Modern Vision Stack9:01
  • 1341 Challenges & Evolution & Historical Context – The Vision Stack8:13
  • 1342 Future Theoretical Directions – From Static Recognition to Causal World Mod3:29
  • Code Eg 1.3 The Modern Vision Stack: Scaling, Pretraining, Prompt-Based Inferenc6:01

Requirements

  • 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

Description

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.

Who this course 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

Report abuse