MLFotoFun uses state of the art Machine Learning models that use Convolutional Neural Networks (CNNs) to classify photos taken with your camera or from your library using one of eight state of the art models. The classification includes the two most probable descriptive labels, as well as the probability associated with each label.
The eight models include: AgeNet (that classifies the age of the human subject); GenderNet (that classifies the gender of the subject); CNN Emotions (that classifies the emotion of the person); VisualSentiment (that classifies the human subject’s sentiment as positive or negative); Food101 (that classifies the food), Oxford102 (that classifies flowers); CarRecognition (that classifies the make of car); and GoogLeNetPlaces (that classifies the category of place in the image).
This app is for entertainment purposes only, and clearly demonstrates how bad such models can be, as well as the biases that they may contain, so no offence is intended with age or gender classification. It may however also surprise you in how far image recognition has come in the five years.
Photo Classifier uses state of the art Machine Learning models that use Convolutional Neural Networks (CNNs) to classify photos taken with your camera or from your library. The classification includes both a descriptive label of the scene, as well as the probability associated with the label.
Six models have been included, and the app provides the output from each of the models in a fraction of a second each. You can see more detail on the top five predictions for each model, along with the probability of each prediction.
This is a simple but very powerful tool that demonstrates how far artificial intelligence and machine learning has come, especially the power of deep neural networks and specifically, convolutional neural networks. These models are all freely available from the internet under the licenses provided by the links below.
GoogLeNetPlaces: Creative Common License. More information available at http://places.csail.mit.edu
Resnet50: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE
Inceptionv3: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE
VGG16: Creative Commons Attribution 4.0 International(CC BY 4.0). More information available at https://creativecommons.org/licenses/by/4.0/
SqueezeNet: BSD License. More information available at https://github.com/DeepScale/SqueezeNet/blob/master/LICENSE
MobileNet: Apache License. Version 2.0 http://www.apache.org/licenses/LICENSE-2.0