Upload 2 files

README.md

@@ -1,12 +1,102 @@
----
-title: Object Detection Using Yolov8
-emoji: 🦀
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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>

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17ffa274de93f7a9dd047ad3c723346ff4f16e21e260cab47d7141367ca259b9
+size 6211256