# Stanford CS231n Lecture 2. Image Classification

Stanfoard CS231n 2017를 요약한 포스팅입니다. 정보 전달보다 자신을 위한 정리 목적이 강한 글입니다! :)

## Image Classification

• Input : Image
• Output : Category Labels
• Semantic Gap
• 이미지에서 추출할 수 있는 정보(색, 질감)와 사람들이 원하는 추상적 정보의 차이
• Challenges
• Viewpoint Variation ( 보는 각도 )
• Illumination ( 조명 )
• Deformation ( 변형 )
• Occlusion ( 은폐, 숨김 )
• Background Clutter ( 배경과 섞임 )
• Intraclass Variation ( 물체의 다양성 )
• 상상하는 모든 이미지를 실시간으로 판단하고 싶음
• Attempts
• Find edges
• Find corners
• brittle

### Data-Driven Approach

• Collect a dataset of images and labels
• Use Maching Learning to train a classifier
• Evaluate the classifier on new images

## Classifier : Nearest Neighbor

• Train : Memorize all data and labels
• Predict : Predict the label of the most similiar training image

### Example Dataset: CIFAR10

• 10 classes(airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
• 50000 training images
• 10,000 testing images

### Distance Metric

• L1(Manhattan) distance
• L2(Euclidean) distance

### Hyper Paramter

• What is the best value of k to use?
• Whate is the best distinct to use?
• Choices about the algorithm that we set rather than learn
• Setting Hyper Paramters
• Idea 1. Choose hyper paramters that work best on your data
• Bad : K=1$K=1$ always work perfectly on training data
• Idea 2. Split data into train and test, choose hyper parameters that work best on test data
• Bad : No idea how algorithm will perform on new data
• Idea 3. Split data into train, val, and test; choose hyper parameters on val and evaluate on test
• Better!
• validation set : check accuracy, check how well algorithm is doing
• idea 4. Cross-Validation: Split data into folds, try each fold as validation and average the results
• Useful for small datasets, but not used too frequently in deep learning

### KNN on images never used

• Very slow at test time
• Distance metrics on pixels are not informative
• Curse of dimensionality ( 차원의 저주 )

### Parametric Approach

• Image : Array of 32\times 32\times 3$32\times 32\times 3$ (3072)
• Function : f(x,W)$f(x,W)$
• Output : 10 numbers giving class scores

### Coming up

• Loss function : quantifying what it means to have a “good” W
• Optimization : start with random W and ifnd a W that minimizes the loss
• ConvNets : tweak the functional form of f

## Reference

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