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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Real-Time Semantic Segmentation in Mobile device
| Date | Stars |
|---|---|
| 2026-07-24 | 721 |
| 2026-07-25 | 721 |
| 2026-07-28 | 721 |
| 2026-07-30 | 721 |
| 2026-08-06 | 721 |
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# Real-Time Semantic Segmentation in Mobile device
This project is an example project of semantic segmentation for mobile real-time app.
The architecture is inspired by [MobileNetV2](https://arxiv.org/abs/1801.04381) and [U-Net](https://arxiv.org/abs/1505.04597).
[LFW, Labeled Faces in the Wild](http://vis-www.cs.umass.edu/lfw/part_labels/), is used as a Dataset.
The goal of this project is to detect hair segments with reasonable **accuracy and speed in mobile device**. Currently, it achieves 0.89 IoU.
About speed vs accuracy, more details are available at [my post](https://medium.com/vitalify-asia/real-time-deep-learning-in-mobile-application-25cf601a8976).
## Example application
* [iOS](https://github.com/vfa-tranhv/MobileAILab-HairColor-iOS)
* Android (TODO)
## Requirements
* Python 3.8
* ```pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html```
* CoreML for iOS app.
## About Model
At this time, there is only one model in this repository, [MobileNetV2_unet](./src/mobile_seg/modules/net.py). As a typical U-Net architecture, it has encoder and decoder parts, which consist of depthwise conv blocks proposed by MobileNets.
Input image is encoded to 1/32 size, and then decoded to 1/2. Finally, it scores the results and make it to original size.
## Steps to training
### Data Preparation
Data is available at LFW. To get mask images, refer [issue #11](../../issues/11) for more. After you got images and masks, put the images of faces and masks as shown below.
```
data/
lfw/
raw/
images/
0001.jpg
0002.jpg
masks/
0001.ppm
0002.ppm
```
### Training
If you use 224 x 224 as input size, pre-trained weight of MobileNetV2 is available. It will be automatically downloaded when you train model with the following command.
```
cd src
python run_train.py params/002.yaml
```
Dice coefficient is used as a loss function.
## Pretrained model
|Input size|IoU|Download|
|----|----|---|
|224|0.89|[Google Drive](https://drive.google.com/file/d/11qBqH79RqCaibuACzZD_b9yIY9fwJinJ/view?usp=sharing)|
## Converting
As the purpose of this project is to make model run in mobile device, this repository contains some scripts to convert models for iOS and Android.
* [run_convert_coreml.py](src/run_convert_coreml.py)
* It converts trained PyTorch model into CoreML model for iOS app.
## TBD
- [x] Report speed vs accuracy in mobile device.
- [ ] Convert pytorch to Android using TesorFlow Light
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5ae95ccc700f7326, topic:deep-learning, topic:pytorch, topic:tensorflow