Update README.md

README.md

@@ -1,3 +1,56 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# YOLOLite-edge_s (ONNX, 640x640)
+
+YOLOLite-edge_s is a lightweight, CPU-oriented object detection model designed for real-time performance on edge devices and industrial systems.  
+The model is part of the YOLOLite family, focusing on **practical latency**, **small model size**, and **stable performance across many real-world datasets**.
+
+---
+
+📦 Full source code: https://github.com/Lillthorin/YoloLite-Official-Repo
+
+📊 Benchmark Results can be found under BENCHMARK.md in the repo
+
+## 🔍 Key Features
+
+- **Real-time CPU performance** (25–30 FPS end-to-end on desktop CPU)
+- **Fast ONNX inference** (14–18 ms per frame)
+- Optimized for **industrial and edge applications**
+- Supports **resize** or **letterbox** preprocessing
+- Reliable performance across 40+ diverse Roboflow100 datasets
+
+---
+
+## ⚡ Real-World Performance (CPU, ONNX Runtime)
+
+Tested using 1080p traffic footage (`intersection.mp4`) and the script  
+`onnx_intersection_showcase.py`.
+
+| Measurement | Result |
+|------------|--------|
+| **End-to-end FPS** | **25–30 FPS** |
+| **Raw ONNX inference** | **14–18 ms** (~55–70 FPS) |
+| **Resolution** | 640 × 640 |
+| **Execution Provider** | CPUExecutionProvider |
+
+These values include the **full pipeline**: video → resize → inference → NMS → drawing.
+
+---
+
+
+## 🧪 Example Usage
+
+```python
+from tools.infer_onnx import ONNX_Predict
+import cv2
+
+predict = ONNX_Predict("edge_s_640_resize.onnx",
+                       providers=["CPUExecutionProvider"],
+                       use_letterbox=False)
+
+frame = cv2.imread("image.jpg")
+boxes, scores, classes = predict.infer_image(frame, img_size=640)
+
+for (x1, y1, x2, y2), score, cls in zip(boxes, scores, classes):
+    print(x1, y1, x2, y2, score, cls)