From e54157bd30198e30c8997130e4a1a5d677707494 Mon Sep 17 00:00:00 2001 From: bhavyachinni <136558331+bhavyachinni@users.noreply.github.com> Date: Sun, 14 Apr 2024 09:41:46 +0530 Subject: [PATCH 1/5] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 791dae7..976e388 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Gender-and-Age-Detection GitHub +# Age-Detection Of Insulators GitHub

Objective :

From bd695e9312e4bc153c33f9d923b5ed63f0bd9d9d Mon Sep 17 00:00:00 2001 From: bhavyachinni <136558331+bhavyachinni@users.noreply.github.com> Date: Sun, 14 Apr 2024 09:53:20 +0530 Subject: [PATCH 2/5] Update README.md --- README.md | 59 +++++++++---------------------------------------------- 1 file changed, 9 insertions(+), 50 deletions(-) diff --git a/README.md b/README.md index 976e388..e2b42e7 100644 --- a/README.md +++ b/README.md @@ -2,10 +2,10 @@

Objective :

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To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture or through webcam.

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To Determine the age of insulator.

About the Project :

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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 models trained by Tal Hassner and Gil Levi. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.

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In this Python Project, I had used Deep Learning to accurately identify the aging of insulators here. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.

@@ -24,16 +24,16 @@

The contents of this Project :

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For face detection, we have a .pb file- this is a protobuf file (protocol buffer); it holds the graph definition and the trained weights of the model. We can use this to run the trained model. And while a .pb file holds the protobuf in binary format, one with the .pbtxt extension holds it in text format. These are TensorFlow files. For age and gender, the .prototxt files describe the network configuration and the .caffemodel file defines the internal states of the parameters of the layers.

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agee detection, we have a .pb file- this is a protobuf file (protocol buffer); it holds the graph definition and the trained weights of the model. We can use this to run the trained model. And while a .pb file holds the protobuf in binary format, one with the .pbtxt extension holds it in text format. These are TensorFlow files. For age and gender, the .prototxt files describe the network configuration and the .caffemodel file defines the internal states of the parameters of the layers.

Usage :

# Working: -[![Watch the video](https://img.youtube.com/vi/ReeccRD21EU/0.jpg)](https://youtu.be/ReeccRD21EU) +

Examples :

NOTE:- I downloaded the images from Google,if you have any query or problem i can remove them, i just used it for Educational purpose.

- >python detect.py --image girl1.jpg - Gender: Female - Age: 25-32 years - - - - >python detect.py --image girl2.jpg - Gender: Female - Age: 8-12 years - - - - >python detect.py --image kid1.jpg - Gender: Male - Age: 4-6 years - - - - >python detect.py --image kid2.jpg - Gender: Female - Age: 4-6 years - - - - >python detect.py --image man1.jpg - Gender: Male - Age: 38-43 years - - - - >python detect.py --image man2.jpg - Gender: Male - Age: 25-32 years - - - - >python detect.py --image woman1.jpg - Gender: Female - Age: 38-43 years - - - + From 8f428322d6b2919ab1047ef32b75e2e89e5200e7 Mon Sep 17 00:00:00 2001 From: bhavyachinni <136558331+bhavyachinni@users.noreply.github.com> Date: Sun, 14 Apr 2024 09:54:31 +0530 Subject: [PATCH 3/5] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e2b42e7..beed48a 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@

Note: The Image should be present in same folder where all the files are present

From efedd0e3a3360e44d09ed3e0144eb14e68fe4195 Mon Sep 17 00:00:00 2001 From: bhavyachinni <136558331+bhavyachinni@users.noreply.github.com> Date: Sun, 14 Apr 2024 09:59:06 +0530 Subject: [PATCH 4/5] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index beed48a..0e2a06b 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@

In this Python Project, I had used Deep Learning to accurately identify the aging of insulators here. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.

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For this python project, I had used the Adience dataset; the dataset is available in the public domain and you can find it here. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, hydrophobic, and appearance. The images have been collected from drones taken /from the data set and distributed under the Creative Commons (CC) license. It has a total of 13,000 photos of Labels :5373 photos containing the Val, Train,Test subjects and is about 1GB in size. The models I used had been trained on this dataset.

Additional Python Libraries Required :