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https://github.com/shicai/MobileNet-Caffe
Caffe Implementation of Google's MobileNets (v1 and v2)
https://github.com/shicai/MobileNet-Caffe
caffe imagenet mobilenet mobilenetv2 mobilnet-v2
Last synced: 8 days ago
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Caffe Implementation of Google's MobileNets (v1 and v2)
- Host: GitHub
- URL: https://github.com/shicai/MobileNet-Caffe
- Owner: shicai
- License: bsd-3-clause
- Created: 2017-04-27T07:29:23.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-06-08T06:28:43.000Z (over 3 years ago)
- Last Synced: 2024-10-16T02:02:41.591Z (24 days ago)
- Topics: caffe, imagenet, mobilenet, mobilenetv2, mobilnet-v2
- Language: Python
- Homepage:
- Size: 27.3 MB
- Stars: 1,258
- Watchers: 65
- Forks: 708
- Open Issues: 47
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-caffe : https://github.com/shicai/MobileNet-Caffe
- awesome-image-classification - unofficial-caffe : https://github.com/shicai/MobileNet-Caffe
README
# MobileNet-Caffe
### Introduction
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](https://arxiv.org/abs/1704.04861)
- [v2] [Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381)
### Pretrained Models on ImageNet
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](http://ethereon.github.io/netscope/#/gist/2883d142ae486d4237e50f392f32994e), [netron](http://lutzroeder.github.io/netron?gist=2883d142ae486d4237e50f392f32994e)
MobileNet v2| 71.90| 90.49| a3124ce7 (13.5 MB)| [netscope](http://ethereon.github.io/netscope/#/gist/d01b5b8783b4582a42fe07bd46243986), [netron](http://lutzroeder.github.io/netron?gist=d01b5b8783b4582a42fe07bd46243986)
### Evaluate Models with a single image
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'
```
### Finetuning on your own data
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
}
}
```
### Related Projects
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](https://github.com/hollance/MobileNet-CoreML)
- Mobile-deep-learning [baidu/mobile-deep-learning](https://github.com/baidu/mobile-deep-learning)
- Receptive Field Block Net for Accurate and Fast Object Detection [ruinmessi/RFBNet](https://github.com/ruinmessi/RFBNet)
- Depthwise Convolutional Layer [yonghenglh6/DepthwiseConvolution](https://github.com/yonghenglh6/DepthwiseConvolution)
- MobileNet-MXNet [KeyKy/mobilenet-mxnet](https://github.com/KeyKy/mobilenet-mxnet)
- Caffe2-MobileNet [camel007/caffe2-mobilenet](https://github.com/camel007/caffe2-mobilenet)
### Updates (Feb. 5, 2018)
- 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`