https://github.com/inborastudio/aigender
Gender Detection Network A highly optimized machine learning project that detects gender from photos using a pre-trained neural network model. Fast and accurate, ready to deploy for image-based gender classification tasks.
https://github.com/inborastudio/aigender
ai aimodel classfication gender-classification gender-recognition neuralnetwork
Last synced: about 1 month ago
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Gender Detection Network A highly optimized machine learning project that detects gender from photos using a pre-trained neural network model. Fast and accurate, ready to deploy for image-based gender classification tasks.
- Host: GitHub
- URL: https://github.com/inborastudio/aigender
- Owner: InboraStudio
- Created: 2024-09-04T18:51:21.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-27T08:45:08.000Z (2 months ago)
- Last Synced: 2025-07-27T10:30:10.639Z (2 months ago)
- Topics: ai, aimodel, classfication, gender-classification, gender-recognition, neuralnetwork
- Language: Python
- Homepage: https://huggingface.co/spaces/DrChamyoung/AIGender
- Size: 54.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Gender
```A Project Build to Test my New GPU 😊 ```
**AI Gender** is a compact, high-performance gender classification project that uses deep learning techniques to detect gender from facial images. This solution is engineered specifically for environments with **limited computational resources** such as:
* Legacy laptops and desktops
* Raspberry Pi devices
* Embedded systems
* Low-cost cloud VMs
The project is available as a **Gradio demo on Hugging Face Spaces**, and can be deployed or integrated into lightweight systems easily.
## About
The core of **AI Gender** is a **pre-trained convolutional neural network** model. It accepts an image input, processes it to extract facial features, and classifies the gender as either male or female.
Unlike large-scale models that require dedicated GPUs, this project prioritizes mathematical efficiency and architectural optimization.
## Mathematical Foundation
* Uses **Convolutional Neural Networks (CNNs)** optimized for small-scale inputs
* Reduces floating point operations (FLOPs) using smaller kernels and fewer channels
* Implements **transfer learning** with frozen backbone layers to minimize active computation
* Model size is trimmed using **quantization-aware training** (QAT)
* Confidence score output is based on softmax probability distribution
Key equations:
1. Convolution Operation:
$(I * K)(x, y) = \sum_m \sum_n I(m, n) \cdot K(x - m, y - n)$
2. Softmax:
$\text{Softmax}(z_i) = \frac{e^{z_i}}{\sum_j e^{z_j}}$
These optimizations ensure the model runs efficiently on minimal hardware.
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## Low-End Technology Support
To make this model runnable on **super low-end tech**, we applied the following:
* **Backbone**: MobileNetV2 / ResNet18 (for small size and speed)
* **RAM Usage**: < 200MB during inference
* **Disk Footprint**: Model file under 10MB
* **CPU Only**: No CUDA or GPU dependencies
* **Lazy Loading**: Loads only necessary components on start
* **Zero External Heavy Libraries**: Avoids OpenCV, dlib, or other memory-intensive tools
This makes it suitable for real-time or batch inference on constrained devices.
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## Demo (Hugging Face)
Try the model online here:
[https://huggingface.co/spaces/your-username/ai-gender](https://huggingface.co/spaces/DrChamyoung/AIGender)
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## Notes
This project was created as an accessible AI showcase, proving that **practical deep learning** can be done without expensive hardware. It’s ideal for classrooms, research demonstrations, and offline AI use cases.