CSE471 — Deep Learning For Computer Vision
Study Units
My Weak QuestionsFoundations of Computer Vision and Deep Learning
Convolutional Neural Networks and Training Techniques
Transfer Learning and Fine-Tuning for Vision Tasks
Object Detection and Localization
Image Segmentation and Advanced Vision Architectures
Generative Vision Models
Continuous Assessment
3 components
Students are required to carry out a comprehensive computer vision project by covering the entire course syllabus, involving problem identification, dataset selection, text preprocessing, model design and implementation using deep learning and transformer-based approaches, performance evaluation using suitable metrics, and analysis of results, followed by proper documentation and presentation of the work.
Week 3 / 11
Rubric
The objective of this project is to apply complete deep learning for computer vision to solve real world problems by designing, implementing and evaluating the techniques.
Unit-I and Unit-II will be covered.
Week 4 / 5
Rubric
Student will be able to learn the basic concepts of computer vision and deep learning along with Convolution Neural Network architecture.
Subjective test covering syllabus from Unit-1 to Unit-IV.
Week 11 / 12
Rubric
To test the subject knowledge of the students and provide opportunity to improve their CA performance in case of low marks or any missed CA.
Exams & Practice
End Term Examination
Final semester comprehensive evaluation
50%Type: Examination
All MCQ
CSE471 - FAQs
How many units are in CSE471?
CSE471 has 6 units. Each unit includes detailed notes and MCQ practice questions.
What exam resources are available for CSE471?
Unit-wise notes and MCQ practice are available. Exam resources coming soon.
How to prepare for CSE471 exams?
Study each unit's notes thoroughly, practice MCQs to test understanding, and attempt mock tests before exams. Focus on important topics and previous year questions.