Round 1: Completed #educational Weight: 20.0
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🕵️ Introduction

In most F1 races, the difference between first and second position is a matter of a fraction of a second. So maintaining your speed is the most important thing!

But what happens when you can’t read the speed dial properly at such a high speed? The next puzzle requires you to take the images of the speedometer and predict the speed of the car. Don’t know how to get started, check out our code kit.

💾 Dataset

The given dataset contains images of speedometer. Each image contains its label i.e. the speed of the F1 CarThe image dimensions of the images 256*256.

📁 Files

Following files are available in the resources section:

• train.zip - (40000 samples) This zip file contains the speedometer 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 speed of F1 car.
• val.zip - (4000 samples) This zip file contains the speedometer 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 speed of F1 car.
• test.zip - (10000 samples) This zip file contains the speedometer 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 speed of F1 car.
• 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 Mean Squared Error will be used to test the efficiency of the model.