In today’s world, mobile applications are becoming increasingly sophisticated, offering features that were once thought possible only on high-powered computing systems. One such advanced capability is semantic segmentation, which involves classifying each pixel in an image into various categories. In this article, we will delve into a project that demonstrates semantic segmentation specifically designed for real-time applications on mobile devices.
Project Overview
This project is a stellar example of implementing semantic segmentation for mobile apps, primarily focusing on detecting hair segments with a commendable accuracy of 0.89 Intersection over Union (IoU). By leveraging advanced architectures such as MobileNetV2 and U-Net, this model strikes an effective balance between accuracy and speed on mobile devices.
Dataset
The dataset used in this project is Labeled Faces in the Wild (LFW). This facial dataset provides the necessary images to train our model effectively.
Example Application
- iOS Application
- Android Application (TODO)
Requirements
- Python 3.8
- Install dependencies:
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
- CoreML for iOS application.
About the Model
The primary model in this repository is named MobileNetV2_unet
. It’s designed as a typical U-Net architecture, which consists of encoder and decoder parts that utilize depthwise convolutional blocks provided by MobileNets. An input image is processed to a size of 132 pixels before being reconstructed back to its original dimensions after scoring.
Steps to Training
Data Preparation
Data is sourced from the LFW dataset. For creating mask images necessary for training, refer to issue #11. Once you have both images and masks, organize them as follows:
data/
lfw/
raw/
images/
0001.jpg
0002.jpg
masks/
0001.ppm
0002.ppm
Training
For training with an input size of 224 x 224 pixels, you can utilize the pre-trained weights of MobileNetV2, which will automatically download during the training process. Use the following command to initiate the training procedure:
cd src
python run_train.py params002.yaml
The training process uses the Dice coefficient as the loss function to ensure the model learns effectively.
Pre-trained Model
Here are the details about the pre-trained model:
- Input Size: 224
- IoU: 0.89
- Download Pre-trained Model
Converting the Model
This project includes scripts to adapt models for mobile applications. The notable script for iOS conversion is:
run_convert_coreml.py
– Converts the trained PyTorch model into a CoreML model for iOS applications.
TBD (To Be Done)
- [x] Report speed vs accuracy on mobile devices.
- [ ] Convert PyTorch model to Android using TensorFlow Lite.
Troubleshooting
If you encounter any issues during the implementation, consider the following troubleshooting ideas:
- Ensure all dependencies are correctly installed as per the requirements.
- Double-check the data paths for images and masks.
- Look for any error messages during training in the console and search for solutions online.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.