rorshark-vit-base
Property | Value |
---|---|
Parameter Count | 85.8M |
License | Apache 2.0 |
Base Model | google/vit-base-patch16-224-in21k |
Accuracy | 99.23% |
Framework | PyTorch 2.1.1 |
What is rorshark-vit-base?
rorshark-vit-base is a fine-tuned Vision Transformer (ViT) model based on the google/vit-base-patch16-224-in21k architecture. This model has been optimized for image classification tasks and demonstrates exceptional performance with a 99.23% accuracy rate on its evaluation dataset.
Implementation Details
The model utilizes a transformer-based architecture specifically designed for computer vision tasks. It was trained using the Adam optimizer with a carefully tuned learning rate of 2e-05 and employs a linear learning rate scheduler. The training process spanned 5 epochs with batch sizes of 8 for both training and evaluation.
- Parameters: 85.8M
- Training Duration: 5 epochs
- Final Validation Loss: 0.0393
- Optimization: Adam (β1=0.9, β2=0.999, ε=1e-08)
Core Capabilities
- High-accuracy image classification (99.23%)
- Efficient processing of visual data using transformer architecture
- Support for TensorBoard visualization
- Compatibility with standard image folder datasets
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy of 99.23% and its foundation on the proven ViT architecture. The careful fine-tuning process has resulted in a very low validation loss of 0.0393, making it highly reliable for image classification tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks requiring high accuracy. Its robust performance makes it ideal for applications in automated visual inspection, content categorization, and other computer vision tasks where precision is crucial.