README.md

Gender Classification using CNN & openCV

This project focuses on classifying the gender of individuals from facial images. It employs a combination of techniques including transfer learning, fine-tuning, and custom CNN models.

Overview

1. Transfer Learning with VGG16

   • Trained on a dataset of 12,000 face images.
   • Achieved 55% accuracy, but was slower than desired.

2. Fine-tuned Model from Hugging Face (rizvandwiki)

   • Improved performance using a pre-trained model specifically designed for gender detection.
   • Provided a good balance of accuracy and speed.

3. Face Detection with OpenCV

   • Utilized OpenCV's in-built harcascade model for face detection.

Selected Model

After training multiple CNN models on a dataset of 12,000+ images with corresponding testing on 5,000+ samples, the following results were obtained:

1. Model 1:

   • Train Accuracy: 86%
   • Test Accuracy: 85%

2. Model 2:

   • Train Accuracy: 90%
   • Test Accuracy: 89%

3. Model 3:

   • Train Accuracy: 85%
   • Test Accuracy: 83%

Given these results, the second model was selected for the final implementation. It demonstrated a commendable balance between training and testing accuracy, achieving 90% and 89% respectively.

Graphical representation of the Loss

Graphical representation of the accuracy

Model Performance

Model	Train Accuracy	Test Accuracy
Fine-tuned VGG16	61%	55%
rizvandwiki's Model	-	-
Custom CNN Model 1	86%	85%
Custom CNN Model 2	90%	89%
Custom CNN Model 3	85%	83%

Application Setup

Download the Best fitted model ~

Click Here

Requirements

• Python 3.x
• OpenCV
• Tensorflow >=2.4
• Transformers
• ThreadPoolExecutor
• keras.applications
• download the model & keep it in your working Directory
• Gender Detection Model by rizvandwiki

How to Use the Fine-tuned model

from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("rizvandwiki/gender-classification-2")
model = AutoModelForImageClassification.from_pretrained("rizvandwiki/gender-classification-2")
inputs = extractor(images = train_images[image_name], return_tensors="pt" )
    with torch.no_grad():
        logits = model(**inputs).logits
        predicted_label = logits.argmax(-1).item()
        label = model.config.id2label[predicted_label]

How to clone the repository & run the file

1. Clone the Repository

   git clone https://github.com/your_username/your_project.git
   cd your_project

2. Install Dependencies

   pip install -r requirements.txt

3. Run the main file

   python gender_detection_app.py

NOTE:

This Project was a part of a College Assignment (Partial). For any inquiry please feel free to concat me or you can raise an issue in the GitHub issue section.

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