resolve conflicts in count.md

docs/solutions/count.md

@@ -27,11 +27,10 @@ keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks
 These advantages highlight the significance of object counting with Ultralytics YOLOv8 in various domains, including retail, manufacturing, traffic management, and more.
 
 ## Real-world Applications
-|                                                     Transportation                                                     |                                                        Retail                                                         |                                                     Aquaculture                                                     |
-|:----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
-| ![Vehicle Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab) | ![People Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527) | ![Fish Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142) |
-|                                                    Vehicle Tracking                                                    |                                                    People Tracking                                                    |                                                    Fish Tracking                                                    |
-
+|                                                     Transportation                                                     |                                                     Aquaculture                                                     |                                                        Retail                                                         |
+|:----------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|
+| ![Vehicle Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/74e4ede4-5766-494d-b4b1-9400cd1809f9) | ![Fish Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cf55ef6e-f4b2-4795-a2bd-2b6ed9c0ab1a) | ![People Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/2e3834c4-605e-4ad9-aab9-9736f1d0f6ca) |
+|                                                    Vehicle Counting                                                    |                                                    Fish Counting                                                    |                                                    People Counting                                                    |
 
 ## Key Features of Object Counting
 
@@ -41,7 +40,7 @@ YOLOv8's counting mode offers robust and versatile object counting:
 
 2. **Flexible Regions:** YOLOv8 allows you to easily customize counting areas to suit your specific needs, offering adaptability for diverse scenarios.
 
-3. **Efficiency & Speed:** With YOLOv8, you can count objects swiftly in real-time, making it a valuable tool for tasks that demand rapid processing.
+3. **Efficiency & Speed:** With YOLOv8, you can count objects swiftly in real time, making it a valuable tool for tasks that demand rapid processing.
 
 ### Example
 
@@ -79,7 +78,7 @@ YOLOv8's counting mode offers robust and versatile object counting:
         ```
 
 ### Note
-The region is movable, If you will click on any part of the region coordinates (x, y, w, h).
+The region is movable, If you click on any part of the region coordinates (x, y, w, h).
 
 ## Tailored Product Integration
 

ultralytics/solutions/README.Md

@@ -0,0 +1,39 @@
+# YOLOv8 Object Counting
+Object counting entails the task of enumerating unique objects or occurrences within an image or video frame, serving as a foundational element in computer vision, with applications spanning crowd management and inventory control. The incorporation of Object Counting into YOLOv8 offers an uncomplicated workflow, enabling real-time inference and enhanced precision.
+
+## Table of Contents
+
+- [Usage Options](#usage-options)
+- [FAQ](#faq)
+
+## Usage Options
+
+- `--source`: Specifies the path to the video file you want to run inference on.
+- `--save-img`: Flag to save the detection results as images.
+- `--weights`: Specifies a different YOLOv8 model file (e.g., `yolov8n.pt`, `yolov8s.pt`, `yolov8m.pt`, `yolov8l.pt`, `yolov8x.pt`).
+
+## FAQ
+
+**1. What is SAHI?**
+
+SAHI stands for Slicing, Analysis, and Healing of Images. It is a library designed to optimize object detection algorithms for large-scale and high-resolution images. The library source code is available on [GitHub](https://github.com/obss/sahi).
+
+**2. Why use SAHI with YOLOv8?**
+
+SAHI can handle large-scale images by slicing them into smaller, more manageable sizes without compromising the detection quality. This makes it a great companion to YOLOv8, especially when working with high-resolution videos.
+
+**3. How do I debug issues?**
+
+You can add the `--debug` flag to your command to print out more information during inference:
+
+```bash
+python yolov8_sahi.py --source "path to video file" --debug
+```
+
+**4. Can I use other YOLO versions?**
+
+Yes, you can specify different YOLO model weights using the `--weights` option.
+
+**5. Where can I find more information?**
+
+For a full guide to YOLOv8 with SAHI see [https://docs.ultralytics.com/guides/sahi-tiled-inference](https://docs.ultralytics.com/guides/sahi-tiled-inference/).