README.md

UMich-EECS498

Overview

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.

Introduction

Course Homepage: EECS 498.008 / 598.008: Deep Learning for Computer Vision

Assignments

• Assignment 1:
  • Q1: PyTorch 101
  • Q2: K-Nearest Neighbors (k-NN)
• Assignment 2:
  • Q1: Linear Classifiers
  • Q2: Two Layer Neural Network
  • Q3: MNIST Challenge Problem
• Assignment 3:
  • Q1: Fully-Connected Neural Networks and Dropout
  • Q2: Convolutional Neural Networks and Batch Normalization
  • Extra Credit: Pytorch Autograd and NN (from fall2020)
• Assignment 4:
  • Q1: One-Stage Object Detector
  • Q2: Two-Stage Object Detector - Faster R-CNN
• Assignment 5:
  • Q1: Image captioning with RNNs and LSTMs
  • Q2: Transformers
• Assignment 6:
  • Q1: Variational AutoEncoders
  • Q2: Generative Adversarial Networks
  • Q3: Network Visualization
  • Q4: Style Transfer

Instruction

本仓库主要包含 win2022 版的课程 slides，6 个 assignments + solutions 等，solutions 都是用 Google Colab TPU 跑出来的。