Ethnicity_Test_v003
| Property | Value |
|---|---|
| Author | cledoux42 |
| Framework | PyTorch |
| Task | Multi-class Classification |
| Carbon Footprint | 6.02g CO2 |
| Downloads | 195,955 |
What is Ethnicity_Test_v003?
Ethnicity_Test_v003 is a vision transformer (ViT) based image classification model specifically designed for ethnicity detection. Developed by cledoux42 using AutoTrain, this model demonstrates strong performance with a 79.6% accuracy rate while maintaining environmental consciousness with low carbon emissions.
Implementation Details
The model utilizes transformer architecture and was trained using the AutoTrain framework with PyTorch backend. It achieves impressive metrics across various evaluation criteria, including a macro F1 score of 0.797 and consistent precision and recall scores of 0.796 across different averaging methods.
- Loss: 0.530
- Accuracy: 0.796
- Macro/Micro/Weighted F1: 0.797/0.796/0.796
- Environmental Impact: 6.0228g CO2 emissions
Core Capabilities
- Multi-class ethnicity classification
- Efficient inference with endpoint support
- Environmentally conscious processing
- Production-ready with high download count
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balanced performance metrics and environmental consciousness, achieving high accuracy while maintaining minimal carbon emissions. It's been widely adopted with nearly 200,000 downloads, indicating strong community trust.
Q: What are the recommended use cases?
The model is specifically designed for ethnicity classification tasks in images, suitable for research, demographic analysis, and various computer vision applications requiring ethnicity detection. It's important to use such tools responsibly and ethically.
