POT-HOLE-DETECTION-YOLOV5

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POT-HOLE-DETECTION-YOLOV5

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This dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

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https://github.com/Tanwar-12/POT-HOLE-DETECTION-YLOLOV5/assets/110081008/be51a282-68b3-4b96-9d59-ebb466a7294a
# POT-HOLE-DETECTION-YOLOV5

### Overview : 
The purpose of this project is to create a Deep Learning model and this dataset could be used for automatically finding and categorizing potholes in city streets so the worst ones can be fixed faster.

### 📁 Dataset Used : 
https://public.roboflow.com/object-detection/pothole

**This dataset consist of only one class: (Pothole)**

# Workflow:
  ## Data Preparation:
  * Total 465 images for training and 133 images for validation present in 2 classes.
  * Create a bounding boxes with the help of label-img And makesense.ai website according to YoloV5.
  * Prepare folder structure that can be accept by YoloV5.
  ![train folders](https://github.com/Tanwar-12/Face-Mask-Detection/assets/110081008/69b19a8e-2f81-4d9b-a762-ffa73ac59be1)
## Steps to use Yolov5:
* Cloning the YoloV5 file from official repository.
* Changing the directory of yolov5
* Installing the dependencies
* Download all versions pre-trained weights.

## Steps Before Training Custom Dataset:
* Go to yolov5/data/.
* Open data.yaml
* Edit the following inside it:

1. Training and Validation file path
 2. Number of classes and Class names.

## Training YOLOV5 Model
* Set images size 640 with batch of 8.
* Train model around 600 epochs .
* Visualise the training metrics with the help of tensorboard.

## Testing Images Using Test Data

![image](https://github.com/Tanwar-12/POT-HOLE-DETECTION-YLOLOV5/assets/110081008/27b11bc0-f684-4a62-a257-28dd3b017d49)

## Testing Video Demo:

https://github.com/Tanwar-12/POT-HOLE-DETECTION-YLOLOV5/assets/110081008/8dad77ab-d3ab-491a-a0a5-b4eb0ab4d2a9

## Just follow☝️ me and Star⭐ my repository 
## Motivated and supported by the works of,
[Miss. Sakshi Tanwar].

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