The Problem: Classification

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Presentation transcript:

The Problem: Classification Classify an image into 1000 possible classes: e.g. Abyssinian cat, Bulldog, French Terrier, Cormorant, Chickadee, red fox, banjo, barbell, hourglass, knot, maze, viaduct, etc. cat, tabby cat (0.71) Egyptian cat (0.22) red fox (0.11) …..

The Data: ILSVRC Imagenet Large Scale Visual Recognition Challenge (ILSVRC): Annual Competition 1000 Categories ~1000 training images per Category ~1 million images in total for training ~50k images for validation Only images released for the test set but no annotations, evaluation is performed centrally by the organizers (max 2 per week)

The Evaluation Metric: Top K-error Top-1 error: 1.0 Top-1 accuracy: 0.0 Top-2 error: 1.0 Top-2 accuracy: 0.0 True label: Abyssinian cat Top-3 error: 1.0 Top-3 accuracy: 0.0 Top-4 error: 0.0 Top-4 accuracy: 1.0 Top-5 error: 0.0 Top-5 accuracy: 1.0 cat, tabby cat (0.61) Egyptian cat (0.22) red fox (0.11) Abyssinian cat (0.10) French terrier (0.03) …..

Top-5 error on this competition (2012)

Alexnet https://www.saagie.com/fr/blog/object-detection-part1

What is happening? https://www.saagie.com/fr/blog/object-detection-part1

SIFT + FV + SVM (or softmax) Feature extraction (SIFT) Feature encoding (Fisher vectors) Classification (SVM or softmax) Convolutional Network (includes both feature extraction and classifier) Deep Learning

The Model: Convolutional Network

Alexnet in Keras

Preprocessing and Data Augmentation

Preprocessing and Data Augmentation 256 256

Preprocessing and Data Augmentation 224x224

Preprocessing and Data Augmentation 224x224

True label: Abyssinian cat

Other Important Aspects Using ReLUs instead of Sigmoid or Tanh Momentum + Weight Decay Dropout (Randomly sets Unit outputs to zero during training) GPU Computation!

Other Important Aspects Using ReLUs instead of Sigmoid or Tanh Momentum + Weight Decay Dropout (Randomly sets Unit outputs to zero during training) GPU Computation!

VGG Network Top-5: Keras: https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 Simonyan and Zisserman, 2014.

GoogLeNet Keras: https://gist.github.com/joelouismarino/a2ede9ab3928f999575423b9887abd14 Szegedy et al. 2014

BatchNormalization Layer

ResNet Sorry, does not fit in slide. http://felixlaumon.github.io/assets/kaggle-right-whale/resnet.png Keras: https://github.com/raghakot/keras-resnet/blob/master/resnet.py

Slide by Mohammad Rastegari