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‎READMI.md

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# Gender-and-Age-Detection
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<h2>Aim of the Project:</h2>
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<p>To build a gender and age detection system using Python for CodeClause as a Intern.</p>
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<h2>About the Project :</h2>
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<p>
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In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the pre-trained models. The gender may be one of ‘Male’ and ‘Female’, and the age may be one of the following ranges- (0-2)', '(4-6)', '(8-12)', '(15-20)', '(20-24)', '(25-40)', '(41-53)', '(60-100)'.
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It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions.</p>
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<h2>Additional Python Libraries Required :</h2>
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<ul>
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<li>OpenCV</li>
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</ul>
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<ul>
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<li>argparse</li>
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</ul>
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<h2>The contents of this Project :</h2>
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<ul>
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<li>opencv_face_detector.pbtxt</li>
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<li>opencv_face_detector_uint8.pb</li>
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<li>age_deploy.prototxt</li>
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<li>age_net.caffemodel</li>
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<li>gender_deploy.prototxt</li>
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<li>gender_net.caffemodel</li>
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<li>identify.py</li>
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</ul>
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## OutPut
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![Output image](/output.png)

‎_config.yml

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theme: jekyll-theme-tactile

‎aditi.jpeg

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‎age_deploy.prototxt

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name: "CaffeNet"
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input: "data"
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input_dim: 1
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input_dim: 3
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input_dim: 227
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input_dim: 227
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layers {
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name: "conv1"
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type: CONVOLUTION
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bottom: "data"
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top: "conv1"
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convolution_param {
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num_output: 96
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kernel_size: 7
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stride: 4
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}
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}
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layers {
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name: "relu1"
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type: RELU
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bottom: "conv1"
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top: "conv1"
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}
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layers {
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name: "pool1"
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type: POOLING
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bottom: "conv1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "norm1"
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type: LRN
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bottom: "pool1"
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top: "norm1"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layers {
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name: "conv2"
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type: CONVOLUTION
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bottom: "norm1"
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top: "conv2"
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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}
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}
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layers {
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name: "relu2"
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type: RELU
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bottom: "conv2"
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top: "conv2"
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}
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layers {
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name: "pool2"
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type: POOLING
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bottom: "conv2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "norm2"
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type: LRN
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bottom: "pool2"
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top: "norm2"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layers {
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name: "conv3"
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type: CONVOLUTION
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bottom: "norm2"
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top: "conv3"
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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}
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}
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layers{
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name: "relu3"
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type: RELU
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bottom: "conv3"
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top: "conv3"
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}
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layers {
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name: "pool5"
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type: POOLING
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bottom: "conv3"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "fc6"
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type: INNER_PRODUCT
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bottom: "pool5"
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top: "fc6"
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inner_product_param {
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num_output: 512
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}
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}
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layers {
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name: "relu6"
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type: RELU
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bottom: "fc6"
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top: "fc6"
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}
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layers {
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name: "drop6"
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type: DROPOUT
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layers {
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name: "fc7"
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type: INNER_PRODUCT
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bottom: "fc6"
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top: "fc7"
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inner_product_param {
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num_output: 512
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}
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}
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layers {
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name: "relu7"
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type: RELU
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bottom: "fc7"
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top: "fc7"
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}
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layers {
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name: "drop7"
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type: DROPOUT
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bottom: "fc7"
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top: "fc7"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layers {
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name: "fc8"
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type: INNER_PRODUCT
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bottom: "fc7"
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top: "fc8"
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inner_product_param {
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num_output: 8
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}
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}
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layers {
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name: "prob"
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type: SOFTMAX
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bottom: "fc8"
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top: "prob"
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}

‎age_net.caffemodel

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‎aniket.jpg

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‎arjun.jpeg

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‎bw.jpeg

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‎gender_deploy.prototxt

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name: "CaffeNet"
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input: "data"
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input_dim: 10
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input_dim: 3
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input_dim: 227
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input_dim: 227
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layers {
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name: "conv1"
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type: CONVOLUTION
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bottom: "data"
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top: "conv1"
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convolution_param {
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num_output: 96
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kernel_size: 7
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stride: 4
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}
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}
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layers {
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name: "relu1"
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type: RELU
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bottom: "conv1"
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top: "conv1"
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}
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layers {
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name: "pool1"
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type: POOLING
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bottom: "conv1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "norm1"
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type: LRN
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bottom: "pool1"
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top: "norm1"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layers {
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name: "conv2"
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type: CONVOLUTION
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bottom: "norm1"
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top: "conv2"
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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}
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}
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layers {
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name: "relu2"
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type: RELU
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bottom: "conv2"
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top: "conv2"
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}
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layers {
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name: "pool2"
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type: POOLING
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bottom: "conv2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "norm2"
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type: LRN
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bottom: "pool2"
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top: "norm2"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layers {
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name: "conv3"
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type: CONVOLUTION
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bottom: "norm2"
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top: "conv3"
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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}
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}
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layers{
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name: "relu3"
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type: RELU
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bottom: "conv3"
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top: "conv3"
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}
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layers {
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name: "pool5"
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type: POOLING
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bottom: "conv3"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layers {
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name: "fc6"
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type: INNER_PRODUCT
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bottom: "pool5"
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top: "fc6"
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inner_product_param {
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num_output: 512
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}
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}
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layers {
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name: "relu6"
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type: RELU
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bottom: "fc6"
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top: "fc6"
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}
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layers {
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name: "drop6"
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type: DROPOUT
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layers {
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name: "fc7"
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type: INNER_PRODUCT
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bottom: "fc6"
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top: "fc7"
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inner_product_param {
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num_output: 512
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}
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}
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layers {
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name: "relu7"
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type: RELU
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bottom: "fc7"
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top: "fc7"
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}
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layers {
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name: "drop7"
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type: DROPOUT
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bottom: "fc7"
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top: "fc7"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layers {
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name: "fc8"
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type: INNER_PRODUCT
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bottom: "fc7"
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top: "fc8"
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inner_product_param {
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num_output: 2
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}
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}
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layers {
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name: "prob"
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type: SOFTMAX
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bottom: "fc8"
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top: "prob"
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}

‎gender_net.caffemodel

43.5 MB
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