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README.md

@@ -1,10 +1,34 @@
-
 ---
 library_name: ultralytics
 tags:
 - image-classification
-- yolov26s-cls
+- yolo11
+- recaptcha
+- security
 - revpass
+license: mit
+metrics:
+- accuracy
 ---
-# YOLOv26s-cls (Revpass Single)
-Custom reCAPTCHA v2 classification model.
+# 🛡️ Revpass-Single (YOLO11m-cls)
+![Badge](https://img.shields.io/badge/Task-Image_Classification-blue) 
+![Badge](https://img.shields.io/badge/Model-YOLO11m--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**: YOLO11m-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