hair segmentation in mobile device
No Data
The architecture was inspired by Real-time deep hair matting on mobile devices
python 3.6 tensorflow-gpu==1.13.1, opencv-python==4.1.0.25, Keras==2.2.4, numpy==1.16.4, scikit-image==0.15.0
install environment in conda:
conda env create -f environment.yml
Download data CelebAMask-HQ and use preprocess in ./data/pre-process-data-CelebAMask-HQ.ipynb to create dataset
Data structure training
├── my-data │ ├── train │ │ ├──image │ │ │ ├── 1.jpg │ │ │ ├── 2.jpg │ │ │ ├── 3.jpg ... │ │ ├──mask │ │ │ ├── 1.jpg │ │ │ ├── 2.jpg │ │ │ ├── 3.jpg ... │ ├── val ... │ ├── test ...
```shell script python train.py [--datadir PATH_FOLDER] [--batchsize BATCHSIZE] [epochs EPOCHS] [--lr LEARNING_RATE] []
optional arguments:
--datadir: path to folder dataset, default ./data
--batchsize: batch size training, default 4
--epochs: number of eposchs, default 5
--lr: learning rate, default 1e-4
--imagesize: size image input, default (224, 224)
--usepretrained: use pretrained, default false
--pathmodel: directory is saved checkpoint, default ./checkpoints
--device: device training model, default 0 (GPU:0), 1(GPU:1), -1(CPU)
```
shell script python evaluate.py
```shell script
python demo.py ``` You will see the predicted results of test image in test/data
shell script # Convert Model to Mobile python convert_to_tflite.py- Show shape model tflite
shell script # Shape input and output shape model tflite python shape_input_output_tflite.py## About Keras
Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Use Keras if you need a deep learning library that:
allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). supports both convolutional networks and recurrent networks, as well as combinations of the two. supports arbitrary connectivity schemes (including multi-input and multi-output training). runs seamlessly on CPU and GPU. Read the documentation Keras.io
Keras is compatible with: Python 3.6.
DOING)
Copyright (c) 2019 Thang Tran Van
Licensed under the MIT License. You may not use this file except in compliance with the License