Create README.md

README.md

@@ -1,102 +1,8 @@
-# Men-wome-detection-using-yolov8
-
-![pexels-kaique-rocha-109919](https://user-images.githubusercontent.com/85225054/218306896-3ce9d1a1-96b0-42f7-8725-c3cbbab39280.jpg)
-
-#### This guide will provide instructions on how to convert OIDv4 data into the YOLO format for use with YOLOv8 object detection algorithms.
-
-#### Getting Started
-
-``` git clone https://github.com/prince0310/Men-wome-detection-using-yolov8-.git ```
-
-
-<details open>
-<summary>Dataset</summary>
-  <br>
-  For training custom data set on yolo model you need to have data set arrangement in yolo format. which includes Images and Their annotation file.<br>
-  
-  ##### clone the repository and run donload the data set and their annotation file 
-  
-  ``` git clone https://github.com/prince0310/OIDv4_ToolKit.git ```
-  
-  ##### Implement ```convert annotation.ipynb``` notebook  <br>
-  
-  it will create data in below format 
-  
-```                
-Custom dataset
-        |
-        |─── train
-        |    |
-        |    └───Images --- 0fdea8a716155a8e.jpg
-        |    └───Labels --- 0fdea8a716155a8e.txt
-        |
-        └─── test
-        |    └───Images --- 0b6f22bf3b586889.jpg
-        |    └───Labels --- 0b6f22bf3b586889.txt
-        |
-        └─── validation
-        |    └───Images --- 0fdea8a716155a8e.jpg
-        |    └───Labels --- 0fdea8a716155a8e.txt
-        |
-        └─── data.yaml
-```
-  
-</details>
-
-
-<details open>
-<summary>Install</summary>
- 
-Pip install the ultralytics package including
-all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
-[**3.10>=Python>=3.7**](https://www.python.org/) environment, including
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
-  
-```bash
-pip install ultralytics
-```
-</details>
-
-<details open>
-<summary>Train</summary>
-  <br>
-  
-Python 
-  
-```bash
-from ultralytics import YOLO
-
-# Train
-model = YOLO("yolov8n.pt")
-
-results = model.train(data="data.yaml", epochs=200, workers=1, batch=8,imgsz=640)  # train the model
-```
-Cli
-  
-```bash
-yolo detect train data=data.yaml model=yolov8n.pt epochs=200 imgsz=640
-  ```
-</details>
-
-<details open>
-<summary>Detect</summary>
-   <br>
-  
-  Python 
-  
-```bash
-from ultralytics import YOLO
-
-# Load a model
-model = YOLO("best.pt")  # load a custom model
-
-# Predict with the model
-results = model("image.jpg", save = True)  # predict on an image
-```
-Cli
-  
-```bash
-yolo detect predict model=path/to/best.pt source="images.jpg"  # predict with custom model
-  ```
-  
-</details>
+title: 📷 Webcam Object Recognition Yolo Coco 🔍 Live Gradio
+emoji: 📷Live
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: false