[PROJECT PROPOSAL]

## Project Request

Develop an object detection model using YOLO. An image will be the input to the model and the model will broadly classify the image into the classes it has been trained on.

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| Field | Deep Learning |
| ------ | --------------------------------- |
| About | An object detection model using YOLO |
| Github | kimix7 |
| Email | kimayashejwalkar8@gmail.com |
| Label | Project Request |

https://github.com/kimix7

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**Define You**

- [x] GSSOC Participant
- [x] Contributor

# Object detection using YOLO

## Description
The object detection project using YOLO (You Only Look Once) focuses on utilizing deep learning techniques to detect and track various objects in images or real-time video streams. By training a YOLO model with a dataset that includes labelled images, the model learns to recognize and localize objects within new images or video frames. This project provides accurate and efficient detection of objects, including people.

## Scope

Objectives:
- Implement a robust and efficient object detection system using YOLO.
- Enable accurate detection and localization of various objects, including specific target objects.
- Learning how YOLO works.

Deliverables
- A trained YOLO model capable of detecting and localizing objects accurately.
- Well-documented guidelines, including dataset preparation, training, inference, and any specific requirements.
- Efficient and real-time object detection on images or video frames.

Constraints
- Availability and quality of labelled training data can impact the model's performance.
- Achieving high accuracy requires sufficient training iterations and careful fine-tuning of the model's parameters. It may involve iterative experimentation and adjustment to optimize detection performance.

## Timeline
Start Date: when assigned
End Date: 10 August