Update README.md

README.md

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-# Yolov6-SAHI
+# Yolov6-SAHI
+
+## Introduction of Yolov6
+YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
+
+<img width="959" alt="image" src="https://user-images.githubusercontent.com/62583018/176825240-abba13fd-94e9-43ed-b7a4-066debd4072f.png">
+
+
+YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference.
+
+YOLOv6 is composed of the following methods:
+
+Hardware-friendly Design for Backbone and Neck
+Efficient Decoupled Head with SIoU Loss
+
+
+github :https://github.com/meituan/YOLOv6
+
+
+
+<div align="center">
+<h1>
+  SAHI: Slicing Aided Hyper Inference
+</h1>
+
+<h4>
+  A lightweight vision library for performing large scale object detection & instance segmentation
+</h4>
+
+<h4>
+    <img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif">
+</h4>
+
+A lightweight vision library for performing large scale object detection & instance segmentation
+
+Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.
+
+
+github:https://github.com/obss/sa