ConvNets

ConvNets

State of the art Convolutional Neural Networks

Artificial Intelligence

Description

State of the art MNIST

A CNN model which achieves 99.7% accuracy. It makes use of Vgg16 model. I've used data augmentation, batch normalization, dropout, maxpooling . The model has been finetuned. Accuracy on training set = 99.42 and accuracy on validation set = 99.45. The model doesn't overfit at all. Three models have been used:

Linear model Single dense layers Vgg style CNN Requirements

keras==1.2.2 Python 2.7

Dogs and cats classification

Made use of Vgg16 model(which won Imagenet competition in 2014).

Models has been finetuned in order to classify image as dog or cat.

Download data:

Test data Training data Acquiring this repo

$ cd ~ $ git clone https://github.com/Convnets $ cd machine-learning-programs

Links

Github

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Prajjwal B. created project ConvNets

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ConvNets

State of the art MNIST

A CNN model which achieves 99.7% accuracy. It makes use of Vgg16 model. I've used data augmentation, batch normalization, dropout, maxpooling . The model has been finetuned. Accuracy on training set = 99.42 and accuracy on validation set = 99.45. The model doesn't overfit at all. Three models have been used:

Linear model Single dense layers Vgg style CNN Requirements

keras==1.2.2 Python 2.7

Dogs and cats classification

Made use of Vgg16 model(which won Imagenet competition in 2014).

Models has been finetuned in order to classify image as dog or cat.

Download data:

Test data Training data Acquiring this repo

$ cd ~ $ git clone https://github.com/Convnets $ cd machine-learning-programs

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