MobileNet-Caffe
This is a Caffe implementation of Google's MobileNets (v1 and v2). For details, please read the following papers:
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):
Network | Top-1 | Top-5 | sha256sum | Architecture |
---|---|---|---|---|
MobileNet v1 | 70.81 | 89.85 | 8d6edcd3 (16.2 MB) | netscope, netron |
MobileNet v2 | 71.90 | 90.49 | a3124ce7 (13.5 MB) | netscope, netron |
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 frameworkhollance/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
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