🕵️ Introduction

Mercedes-AMG and Red Bull Racing are two of the fiercest rivals in recent times. They are constantly trying to one-up each other and gain a significant advantage. They often spend lots of time and research in developing the best car chassis (body) to get that edge on the track.

For this puzzle, you’re given images of the F1 car chassis (body) and based on that you need to classify whether the car body belongs to Red Bull or Mercedes. Check out the starter code kit to get started on this classic binary image classification problem.

💾 Dataset

The given dataset contains images of two different F1 Teams i.e. Redbull and Mercedes of size 265*256 in jpg format. The images in train.zip and val.zip  have their labels i.e. which team it is in train.csv and val.csv. The labels for the images in test.zip needs to be predicted.

📁 Files

Following files are available in the resources section:

• train.zip - (40000 samples) This zip file contains f1 images with images name corresponding to ImageID column of train.csv
• train.csv - (40000 samples) This csv file contains the ImageID column corresponding to train.zip and label column as the name of the team.
• val.zip - (4000 samples) This zip file contains f1 images with images name corresponding to ImageID column of val.csv
• val.csv - (4000 samples) This csv file contains the ImageID column corresponding to val.zip and label column as the name of the team.
• test.zip - (10000 samples) This zip file contains f1 images which will be used to evaluate the performance of the model

🚀 Submission

• Prepare a CSV containing ImageID column corresponding to test.zip and label column as the team name.
• The name of the above file should be submission.csv.
• Sample submission format available at sample_submission.csv in the resources section.

Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During evaluation F1 score is used as Primary Score and Accuracy Score as Secondary Score will be used to test the efficiency of the model.

🔗 Links

• 💪 Challenge Page: https://www.aicrowd.com/challenges/team-classification
• 🗣️ Discussion Forum: https://www.aicrowd.com/challenges/team-classification/discussion
• 🏆 Leaderboard: https://www.aicrowd.com/challenges/team-classification/leaderboards

📱 Contact

• Shubhamai