Object Detection with COCO dataset

[Panchadip-128]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Object Detection with COCO dataset :

🔴 To classify objects based on thier type, detect multiple objects in images or video streams and can be further fine-tuned for custom datasets

🔴 COCO dataset (readily available)

🔴 Approach : The intent of this project is to develop a robust, real-time object detection system using the YOLOv5 (You Only Look Once) model, which is renowned for its speed and accuracy in detecting multiple objects within images or video streams. This system aims to provide a complete workflow for object detection, starting from model training to deployment for real-time applications.

The core objectives include:

Real-time Object Detection: Leveraging YOLOv5's real-time capabilities, the system can detect objects in images or video streams with high accuracy.
Custom Dataset Training: The project supports training YOLOv5 on custom datasets, allowing fine-tuning for specific object detection tasks.
Evaluation: Performance is measured using key metrics such as Precision, Recall, IoU, and mAP, ensuring the model's reliability.
Real-Time Inference: The project is designed for real-time detection, particularly in video streams, where GPU usage is preferred for faster inference.
Model Robustness: Augmentation techniques are incorporated to test the model's robustness, making it adaptable to various real-world scenarios.
The project is built on Python, utilizing PyTorch for deep learning, and integrates essential libraries like OpenCV and Albumentations for image processing and augmentation. This system is ideal for applications requiring real-time object detection in dynamic environments, such as surveillance, autonomous driving, and more.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name : Panchadip Bhattacharjee
• GitHub Profile Link : Panchadip-128
• Email ID :[email protected]
• Participant ID (if applicable):
• Approach for this Project :This project implements an object detection system using the YOLOv5 (You Only Look Once) model. YOLOv5 is a state-of-the-art, real-time object detection algorithm that is both fast and accurate. This system can detect multiple objects in images or video streams and can be further fine-tuned for custom datasets. It includes training the YOLOv5 model, evaluating it on a test dataset, and running real-time inference.
• What is your participant role? (Mention the Open Source program): GSSoC Extd Contributor

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Similar problem statement is already present here in this repository. Hence closing this issue.