1、苹果官方提供的model
modelMobileNet
MobileNets are based on a streamlined architecture that have depth-wise separable convolutions to build lightweight, deep neural networks. Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more.
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SqueezeNet
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more. With an overall footprint of only 5 MB, SqueezeNet has a similar level of accuracy as AlexNet but with 50 times fewer parameters.
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Places205-GoogLeNet
Detects the scene of an image from 205 categories such as an airport terminal, bedroom, forest, coast, and more.
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ResNet50
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more.
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Inception v3
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more.
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VGG16
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more.
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2、第三方共享的model
2.1 Image Processing
Models that takes image data as input and output useful information about the image.
MobileNet
The network from the paper 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications', trained on the ImageNet dataset.
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GoogLeNetPlaces
Detects the scene of an image from 205 categories such as airport, bedroom, forest, coast etc.
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Inceptionv3
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, person etc. The top-5 error from the original publication is 5.6%.
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Resnet50
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, person etc. The top-5 error from the original publication is 7.8%.
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VGG16
Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, person etc. The top-5 error from the original publication is 7.4%.
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CarRecognition
Predict the brand & model of a car.
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TinyYOLO
The Tiny YOLO network from the paper 'YOLO9000: Better, Faster, Stronger' (2016), arXiv:1612.08242
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AgeNet
Age Classification using Convolutional Neural Networks
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GenderNet
Gender Classification using Convolutional Neural Networks
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MNIST
Predicts a handwritten digit.
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CNNEmotions
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
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VisualSentimentCNN
Fine-tuning CNNs for Visual Sentiment Prediction
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Food101
This model takes a picture of a food and predicts its name
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Oxford102
Classifying images in the Oxford 102 flower dataset with CNNs
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FlickrStyle
Finetuning CaffeNet on Flickr Style
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RN1015k500
Predict the location where a picture was taken.
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Nudity
Classifies an image either as NSFW (nude) or SFW (not nude)
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2.2、Style Transfer
Models that transform image to specific style.
HED_so
Holistically-Nested Edge Detection. Side outputs
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FNS-Candy
Feedforward style transfer https://github.com/jcjohnson/fast-neural-style
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FNS-Feathers
Feedforward style transfer https://github.com/jcjohnson/fast-neural-style
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FNS-La-Muse
Feedforward style transfer https://github.com/jcjohnson/fast-neural-style
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FNS-The-Scream
Feedforward style transfer https://github.com/jcjohnson/fast-neural-style
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FNS-Udnie
Feedforward style transfer https://github.com/jcjohnson/fast-neural-style
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FNS-Mosaic
Feedforward style transfer https://github.com/jcjohnson/fast-neural-style
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AnimeScale2x
Process a bicubic-scaled anime-style artwork
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2.3、Text Analysis
Models that takes text data as input and output useful information about the text.
SentimentPolarity
Sentiment polarity LinearSVC.
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DocumentClassification
Classify news articles into 1 of 5 categories.
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MessageClassifier
Detect whether a message is spam.
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NamesDT
Gender Classification using DecisionTreeClassifier
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2.4、Others
Exermote
Predicts the exercise, when iPhone is worn on right upper arm.
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GestureAI
GestureAI
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