vit-xray-pneumonia-classification
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| License | Apache-2.0 |
| Base Model | google/vit-base-patch16-224-in21k |
| Accuracy | 97.42% |
What is vit-xray-pneumonia-classification?
This is a specialized Vision Transformer (ViT) model fine-tuned for detecting pneumonia in chest X-ray images. Built upon Google's ViT-base architecture, it achieves remarkable accuracy of 97.42% in distinguishing between normal and pneumonia cases. The model processes X-ray images using a patch-based approach characteristic of Vision Transformers.
Implementation Details
The model was trained using a carefully optimized process with the following specifications: learning rate of 5e-05, batch size of 64 (16 per device with 4 gradient accumulation steps), and linear learning rate scheduling with 10% warmup. Training ran for 15 epochs with early stopping implemented to prevent overfitting.
- Uses PyTorch framework with F32 tensor precision
- Implements early stopping with 3 epochs patience
- Features TensorBoard integration for training monitoring
- Employs the Adam optimizer with betas=(0.9,0.999)
Core Capabilities
- Binary classification of chest X-rays (Normal vs. Pneumonia)
- High-confidence predictions with probability scores
- Processes standard medical imaging formats
- Supports batch processing for multiple images
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful Vision Transformer architecture with specialized medical imaging capabilities, achieving exceptional accuracy (97.42%) in pneumonia detection. Its implementation of early stopping and careful hyperparameter tuning makes it particularly robust for clinical applications.
Q: What are the recommended use cases?
The model is specifically designed for medical imaging analysis, particularly in screening chest X-rays for pneumonia. It can be integrated into clinical decision support systems, research applications, or automated triage systems. However, it should be used as an assistive tool alongside professional medical judgment.
