autotrain_fashion_mnist_vit_base
| Property | Value |
|---|---|
| Task Type | Image Classification |
| Framework | PyTorch |
| Dataset | Fashion MNIST |
| CO2 Emissions | 0.244 grams |
| Test Accuracy | 94.31% |
What is autotrain_fashion_mnist_vit_base?
This is a Vision Transformer (ViT) model specifically trained on the Fashion MNIST dataset using AutoTrain. It represents a modern approach to image classification, leveraging transformer architecture for fashion item recognition with impressive accuracy metrics.
Implementation Details
The model was trained using AutoTrain's pipeline and achieved exceptional performance metrics, including 94.31% accuracy on the test set. It demonstrates balanced performance across precision (94.35%), recall (94.31%), and F1 score (94.31%), indicating robust and consistent classification capabilities.
- Architecture: Vision Transformer (ViT) Base configuration
- Training Framework: AutoTrain with PyTorch backend
- Environmental Impact: Low carbon footprint (0.244g CO2)
- Validation Loss: 0.168
Core Capabilities
- Multi-class Classification of Fashion Items
- High Precision and Recall across all classes
- Balanced performance metrics (Micro, Macro, and Weighted)
- Production-ready with inference endpoints support
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of Vision Transformers with AutoTrain's automated training pipeline, achieving high accuracy while maintaining a remarkably low carbon footprint. Its balanced performance metrics make it particularly reliable for real-world fashion classification tasks.
Q: What are the recommended use cases?
The model is ideal for fashion item classification, e-commerce product categorization, and automated fashion inventory management systems. With its high accuracy and balanced metrics, it's suitable for both research and production environments.
