Image Classification
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- Intro to Image Classification data-driven approach pipeline
- Nearest Neighbor Classifier
- k-Nearest Neighbor
- Validation sets Cross-validation hyperparameter tuning
- ProsCons of Nearest Neighbor
Intro to Image Classification, data-driven approach, pipeline
data-driven approach
training dataset: labeled images
image classification pipeline: input training set(N images labeled with one of K classess)->learning training a classifier/learning a model->evaluation predict labels of a new set of images
Nearest Neighbor Classifier
compare the image pixel by pixle and add up difference. calculate L1 distance/L2 distance etc.
k-Nearest Neighbor/
find the top k closest images->vote on the label
decision boundaries
Validation sets, Cross-validation, hyperparameter tuning
hyperparameters:cannot use test set to tweak hyperparameters
generalization
overfit
tune hyperparameters: split training set in two(validation set (slightly smaller)&training set)->choose best k
cross-validation:iterate over different validation sets, average the performance
**in practice**avoid cross-validation,usually use 50%-90% of training data to train, rest to validate.
Pros/Cons of Nearest Neighbor
just store, take no time to train. predicting takes too much time
- Image Classification
- image classification 资源摘录
- CS231n-01-Image Classification
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- Multiple Kernels for Image Classification
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