Pedestrian Gender Recognition Model
| Property | Value |
|---|---|
| Parameter Count | 86.2M |
| License | Apache 2.0 |
| Accuracy | 91.07% |
| Framework | PyTorch, Transformers |
What is pedestrian_gender_recognition?
This is a specialized computer vision model fine-tuned from Microsoft's BEiT architecture for identifying gender in pedestrian images. Built on the PETA dataset, it demonstrates exceptional accuracy in gender classification tasks, making it particularly valuable for various surveillance and demographic analysis applications.
Implementation Details
The model is implemented using the Transformers library and PyTorch framework, utilizing the BEiT base architecture with patch size 16 and 224x224 image resolution. It was trained for 5 epochs using the Adam optimizer with a learning rate of 2e-05 and achieved a final validation loss of 0.2170.
- Architecture: BEiT-based vision transformer
- Training Batch Size: 8
- Optimization: Adam (β1=0.9, β2=0.999, ε=1e-08)
- Learning Rate Schedule: Linear decay
Core Capabilities
- High-accuracy gender recognition (91.07%)
- Efficient processing of pedestrian images
- Easy integration via Transformers pipeline
- Support for both ONNX and Safetensors formats
Frequently Asked Questions
Q: What makes this model unique?
The model's high accuracy (91.07%) and specialized focus on pedestrian gender recognition, combined with its implementation using the advanced BEiT architecture, makes it particularly effective for real-world applications.
Q: What are the recommended use cases?
The model is ideal for demographic analysis in surveillance systems, crowd monitoring applications, and automated pedestrian analysis where gender recognition is required. It can be easily integrated into existing pipelines using the Transformers library.
