This is a Caffe implementation of Google's MobileNets (v1 and v2). For details, please read the following papers:

• [v1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• [v2] Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

We provide pretrained MobileNet models on ImageNet, which achieve slightly better accuracy rates than the original ones reported in the paper.

The top-1/5 accuracy rates by using single center crop (crop size: 224x224, image size: 256xN):

Evaluate MobileNet v1:

python eval_image.py --proto mobilenet_deploy.prototxt --model mobilenet.caffemodel --image ./cat.jpg

Expected Outputs:

0.42 - 'n02123159 tiger cat'
0.08 - 'n02119022 red fox, Vulpes vulpes'
0.07 - 'n02119789 kit fox, Vulpes macrotis'
0.06 - 'n02113023 Pembroke, Pembroke Welsh corgi'
0.06 - 'n02123045 tabby, tabby cat'

Evaluate MobileNet v2:

python eval_image.py --proto mobilenet_v2_deploy.prototxt --model mobilenet_v2.caffemodel --image ./cat.jpg

Expected Outputs:

0.26 - 'n02123159 tiger cat'
0.22 - 'n02124075 Egyptian cat'
0.15 - 'n02123045 tabby, tabby cat'
0.04 - 'n02119022 red fox, Vulpes vulpes'
0.02 - 'n02326432 hare'

Modify deploy.prototxt and save it as your train.prototxt as follows: Remove the first 5 input/input_dim lines, and add Image Data layer in the beginning like this:

layer {
 name: "data"
 type: "ImageData"
 top: "data"
 top: "label"
 include {
 phase: TRAIN
 }
 transform_param {
 scale: 0.017
 mirror: true
 crop_size: 224
 mean_value: [103.94, 116.78, 123.68]
 }
 image_data_param {
 source: "your_list_train_txt"
 batch_size: 32 # your batch size
 new_height: 256
 new_width: 256
 root_folder: "your_path_to_training_data_folder"
 }
}

Remove the last prob layer, and add Loss and Accuracy layers in the end like this:

layer {
 name: "loss"
 type: "SoftmaxWithLoss"
 bottom: "fc7"
 bottom: "label"
 top: "loss"
}
layer {
 name: "top1/acc"
 type: "Accuracy"
 bottom: "fc7"
 bottom: "label"
 top: "top1/acc"
 include {
 phase: TEST
 }
}
layer {
 name: "top5/acc"
 type: "Accuracy"
 bottom: "fc7"
 bottom: "label"
 top: "top5/acc"
 include {
 phase: TEST
 }
 accuracy_param {
 top_k: 5
 }
}

MobileNet in this repo has been used in the following projects, we recommend you to take a look:

• The MobileNet neural network using Apple's new CoreML framework hollance/MobileNet-CoreML
• Mobile-deep-learning baidu/mobile-deep-learning
• Receptive Field Block Net for Accurate and Fast Object Detection ruinmessi/RFBNet
• Depthwise Convolutional Layer yonghenglh6/DepthwiseConvolution
• MobileNet-MXNet KeyKy/mobilenet-mxnet
• Caffe2-MobileNet camel007/caffe2-mobilenet

• Add pretrained MobileNet v2 models (including deploy.prototxt and weights)
• Hold pretrained weights in this repo
• Add sha256sum code for pretrained weights
• Add some code snippets for single image evaluation
• Uncomment engine: CAFFE used in mobilenet_deploy.prototxt
• Add params (lr_mult and decay_mult) for Scale layers of mobilenet_deploy.prototxt
• Add prob layer for mobilenet_deploy.prototxt