README.md

---
library_name: ultralytics
tags:
- image-classification
- yolo11
- recaptcha
- security
- revpass
license: mit
metrics:
- accuracy
---
# 🛡️ Revpass-Single (YOLOv26s-cls)
![Badge](https://img.shields.io/badge/Task-Image_Classification-blue) 
![Badge](https://img.shields.io/badge/Model-YOLOv26s--cls-green)
![Badge](https://img.shields.io/badge/Accuracy-90.0%25_(Top--2)-brightgreen)
![Badge](https://img.shields.io/badge/License-MIT-orange)


**Revpass-Single** is a highly optimized single-tile classifier designed to identify reCAPTCHA v2 tile contents. It is a core component of the **Revpass** AI solver system.
## 📊 Performance
- **Model Architecture**: YOLOv26s-cls (Medium)
- **Top-2 Accuracy**: **90.0%** (Verified on Stratified Validation Set)
- **Use Case**: Filtering "Best Match" tiles for 4x4 grids.
## 🚀 Usage
```python
from ultralytics import YOLO
# Load the model
model = YOLO("[https://huggingface.co/saifyxpro/revpass-single/resolve/main/revpass-single.pt](https://huggingface.co/saifyxpro/revpass-single/resolve/main/revpass-single.pt)")
# Inference
results = model("path/to/tile.jpg")
print(results[0].probs.top1conf)
📂 Files
revpass-single.pt: PyTorch weights (Best).
revpass-single.onnx: ONNX export for high-performance inference.
Generated by Revpass Auto-Trainer