All Projects → jocialiang → gender_classifier

jocialiang / gender_classifier

Licence: GPL-3.0 license
Deep learning, Face detection, CNN, Tensorflow, Keras, OpenCV, Python crawler

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Gender_classifier

A gender classifier with 94% accuracy of testing sets has been trained with 6000 faces

Usage

  • crawling_image.ipynb : crawling images by HTTP request
  • haarCascade_face_detection.ipynb : implements face detection by different harrcascade classifier
  • extract_and_save_face.ipynb : detect faces in the image, then crop and save
  • train_gender_classifier.ipynb : implements CNN model and training
  • vgg_pre_trained_model.ipynb : implements VGG16 model with weights pre-trained on ImageNet, but not suggest to only a few classes
  • data_geneterator.ipynb : generate batches of tensor image data with real-time data augmentation
  • rectangle_face_mark_gender.ipynb : implements face detection, then add rectangle and mark gender to different faces in the image
  • gender_classify_middle_hiar_man.h5 : training weights of this classifier

Dependencies

  • Python 3.5+
  • Tensorflow 1.2+
  • Keras 2.0+
  • OpenCV 3.1+
  • numpy, Pandas, PIL, matplotlib, requests
  • Anaconda 4.3, CPU: i7-4790 3.60GHz, GPU: GeForce GTX750, CUDA 8.0, cuDNN 5.0

Results

Alt text

Alt text

Environment setup

Running deep learning model with GPU acceleration

  • Windows
  1. Is your VGA CUDA-Enabled? https://developer.nvidia.com/cuda-gpus
  2. Install CUDA https://developer.nvidia.com/cuda-downloads
  3. Install cuDNN https://developer.nvidia.com/cudnn
  • add ./cudnn/cuda/bin/cudnn64_5.dll to $PATH
  1. Install Anaconda https://www.anaconda.com/download/
  2. Create tensorflow-gpu shell, install tensorflow, keras and OpenCV by the following scripts
  • cmd
  • conda create --name tensorflow-gpu python=3.5 anaconda
  • activate tensorflow-gpu
  • pip install tensorflow-gpu
  • pip install keras
  • conda install -c menpo opencv3
  • python
  • import tensorflow, keras, cv2
  • tensorflow.__version__ (check version)
  • keras.__version__
  • cv2.__version__ (check OpenCV version)
  • deactivate tensorflow-gpu (leave shell)
  • Linux(Ubuntu16.04)
  1. nvidia-smi (check VGA spec.)
  2. apt-get update
    apt-get upgrade
  3. install cuda
  4. install cudnn
  5. install anaconda
  6. Create tensorflow-gpu shell. Install tensorflow, keras and OpenCV by the following scripts
  • conda create -n tensorflow-gpu pyton=3.5
  • source activate tensorflow-gpu
  • conda install anaconda
  • conda install -c conda-forge tensorflow-gpu
  • conda install --channel https://conda.anaconda.org/menpo opencv3
  • python
  • import tensorflow, keras, cv2
  • tensorflow.__version__ (check version)
  • keras.__version__
  • cv2.__version__ (check OpenCV version)
  • source deactivate tensorflow-gpu (leave shell)
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