#مقاله منتشر شده توسط گوگل
#MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
(Submitted on 17 Apr 2017)
We present a class of efficient models called MobileNets for #mobile and embedded #vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between #latency and #accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
🔗 https://arxiv.org/pdf/1704.04861.pdf
#deep_learning #cnn #convolutional_neutral_network
#MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
(Submitted on 17 Apr 2017)
We present a class of efficient models called MobileNets for #mobile and embedded #vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between #latency and #accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
🔗 https://arxiv.org/pdf/1704.04861.pdf
#deep_learning #cnn #convolutional_neutral_network
Advice:
#TensorFlow is a safe bet for most projects. Not perfect but has huge community, wide usage. Maybe pair with high-level wrapper (#Keras, #Sonnet, etc)
I think #PyTorch is best for #research. However still new, there can be rough patches.
Use TensorFlow for one graph over many machines
✔️Consider #Caffe, #Caffe2, or TensorFlow for production deployment
✔️Consider TensorFlow or Caffe2 for #mobile
🖇source: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf
#deep_learning #framework
#TensorFlow is a safe bet for most projects. Not perfect but has huge community, wide usage. Maybe pair with high-level wrapper (#Keras, #Sonnet, etc)
I think #PyTorch is best for #research. However still new, there can be rough patches.
Use TensorFlow for one graph over many machines
✔️Consider #Caffe, #Caffe2, or TensorFlow for production deployment
✔️Consider TensorFlow or Caffe2 for #mobile
🖇source: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf
#deep_learning #framework