Deep Learning for Computer Vision [Stanford CS231n]

Course Website

Deep Learning for CV [Stanford CS231n]

latest, 2022, 16-17 (CS131 CV: Foundations and Applications)

Project introduction

Setup

Environment

• Google Colab
  • call deployColab( ), see more at Colab Tutorial from CS231n.
• Local PC (without GPU is fine in general)
  • Dependency: Python3.9
  • Setup.sh

Dataset cs231n/datasets run the shell to download files.

Details and Note

Assignments (2022) Implementation Details and Note

• Image Classification + Localization $(x,y,w,h)$
• Object Detection
  • Variable numbers of output, sliding window
• Semantic / Instance Segmentation
• Image Captioning

Overview

Visualize the CIFAR-10 dataset

idxs = np.random.choice(idxs, samples_per_class, replace=False) 

• Image Classification (Supervised Learning, Discrete label)

  • kNN
    • 
  • Softmax
  • Multi-class SVM
  • MLP
  • CNN
  • Cross Validation

• Cross Validation for hyperparameter

  • Split data into train, val; choose hyperparameters on val and evaluate on test

Two-Layer Neural Network architecure: affine - relu - affine - softmax.

• Batch/Layer Normalization
• Dropout
• Convolutional Neural Networks

Convolution, Max Pool, Normalization

• PyTorch on CIFAR-10
• Network Visualization: Saliency Maps, Class Visualization, and Fooling Images

See more:

Assignments (2022) Implementation Details and Note

Machine Learning Knowledge Sharing

See more ML_Guidance_Repo about Machine Learning and Data Science in general