stanford-car-vit-patch16
| Property | Value |
|---|---|
| Parameter Count | 85.9M |
| Model Type | Vision Transformer |
| License | Apache 2.0 |
| Tensor Type | F32 |
| Author | therealcyberlord |
What is stanford-car-vit-patch16?
stanford-car-vit-patch16 is a Vision Transformer (ViT) model specifically fine-tuned for car classification tasks. Based on the google/vit-base-patch16-224 architecture, this model has been optimized to identify 196 different classes of cars, including specific makes, models, and years. The model demonstrates impressive performance with an 86% accuracy rate on the testing dataset.
Implementation Details
The model utilizes the Vision Transformer architecture with 16x16 pixel patches and has been trained on the Stanford Car Dataset, which contains 16,185 images split across training (8,144), testing (6,041), and validation (2,000) sets. The implementation leverages PyTorch and the Transformers library for efficient processing and deployment.
- Built on ViT base architecture with patch size 16
- 85.9M parameters for comprehensive feature extraction
- F32 tensor type for precise computations
- Implements Transformer-based image processing
Core Capabilities
- Classification of 196 different car classes
- Detailed make, model, and year identification
- 86% accuracy on test dataset
- Efficient processing of image inputs
- Easy integration with Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of Vision Transformers with specialized fine-tuning for car classification, achieving high accuracy while maintaining the flexibility of the ViT architecture. It's particularly notable for its ability to distinguish between subtle differences in car models and years.
Q: What are the recommended use cases?
The model is ideal for automotive applications requiring detailed car classification, such as parking management systems, vehicle inventory systems, and automotive research. However, it's important to note that the model doesn't cover newer car models beyond the Stanford Car Dataset's scope.
