MLware_Technex24
Welcome to MLware'24, hosted by Technex, the annual technical festival of the Indian Institute of Technology (IIT) BHU, Varanasi. Here, we invite you to participate in a unique challenge where you'll harness the power of machine learning to conquer real-world problems.
About Technex: Technex is an esteemed platform orchestrated by the students of IIT BHU, Varanasi. It serves as a melting pot of innovation, creativity, and technical prowess. The festival features an array of competitions, workshops, guest lectures, and exhibitions, attracting bright minds from across the nation. Technex isn't just an event; it's a celebration of technology, fostering learning, growth, and excellence.
About MLWare'24: MLWare'24 is a segment of Technex designed for those who dare to play with the data that rules the world. It's a gathering of experts and enthusiasts who leverage advanced techniques and algorithms to tackle real-world problems or optimize models. In this edition, we present a challenge centered around Object Character Recognition (OCR). Your task is to train a model capable of accurately predicting characters in a captcha image.
Problem Statement: Given an image of a captcha, your goal is to develop a model that can correctly predict the characters within it. This challenge will test your ability to preprocess data, design and train machine learning models, and optimize their performance. Participants will vie for prestige and prizes by demonstrating exceptional accuracy and efficiency on a provided dataset.
Join us in this exhilarating journey of innovation and problem-solving. Let's push the boundaries of what's possible with machine learning and make a meaningful impact together.
For more details and updates, visit the official Technex website: Technex Website
Repository Contents:
- Dataset: Contains the captcha images for training and testing.
- Code: Includes the code for preprocessing, model training, and evaluation.
- README.md: You're reading it! Provides an overview of the MLware'24 challenge and instructions for participation.
Getting Started:
- Clone this repository to your local machine.
- Navigate to the code directory and run the provided scripts to preprocess data and train your model.
- Test your model's performance on the provided test dataset.
- Submit your trained model and results for evaluation.
Note: Make sure to adhere to the guidelines and submission instructions provided in the competition rules.
Let's embark on this exciting journey of machine learning and problem-solving. Together, let's shape the future with technology.
Happy coding and good luck!