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https://github.com/arya920/gender-detection-project
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.
https://github.com/arya920/gender-detection-project
cnn-classification cnn-for-visual-recognition gender-classification huggingface-transformers image-classification transfer-learning transformer vgg16-model
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/arya920/gender-detection-project
- Owner: Arya920
- Created: 2023-10-07T04:53:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-07T05:56:11.000Z (about 1 year ago)
- Last Synced: 2023-10-07T06:24:55.788Z (about 1 year ago)
- Topics: cnn-classification, cnn-for-visual-recognition, gender-classification, huggingface-transformers, image-classification, transfer-learning, transformer, vgg16-model
- Language: Jupyter Notebook
- Homepage:
- Size: 4.63 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html) 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](https://drive.google.com/file/d/1YhnwqgYIVEd92hvoZwwsxpq2qDEM65Q9/view?usp=sharing)
### 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](https://huggingface.co/rizvandwiki/gender-classification-2)
### How to Use the Fine-tuned model
```bash
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**
```bash
git clone https://github.com/your_username/your_project.git
cd your_project
```
2. **Install Dependencies**
```bash
pip install -r requirements.txt
```
3. **Run the main file**
```bash
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.
[MY LINKEDIN](https://www.linkedin.com/in/arya-chakraborty2002/)
[MY MAIL ID]([email protected])