vit-base-cats-vs-dogs
| Property | Value |
|---|---|
| Base Model | google/vit-base-patch16-224-in21k |
| Accuracy | 98.83% |
| Training Loss | 0.0369 |
| Model Type | Vision Transformer (ViT) |
| Author | akahana |
What is vit-base-cats-vs-dogs?
vit-base-cats-vs-dogs is a fine-tuned Vision Transformer model specifically designed for binary classification between cats and dogs. Built upon Google's ViT-base architecture, this model demonstrates exceptional performance with a remarkable 98.83% accuracy on the evaluation dataset.
Implementation Details
The model utilizes the Vision Transformer architecture with 16x16 pixel patches and maintains the original 224x224 input resolution. It was trained using the Adam optimizer with a learning rate of 0.0002 and linear scheduling, achieving impressive results in just one epoch of training.
- Trained with batch size of 8 for both training and evaluation
- Uses the standard ViT feature extractor for image preprocessing
- Implements patch-based image tokenization for transformer processing
Core Capabilities
- High-accuracy binary classification between cats and dogs
- Efficient image processing with ViT architecture
- Simple integration with the Transformers library
- Robust feature extraction capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy (98.83%) in cat vs dog classification while leveraging the powerful Vision Transformer architecture, making it particularly reliable for this specific use case.
Q: What are the recommended use cases?
The model is specifically designed for binary classification between cats and dogs in images. It's ideal for applications requiring automated pet detection, image sorting, or as part of larger pet-related computer vision systems.
