Fashion MNIST Classification Challenge
Classify the images in the dataset belonging to their respective categories.
One of the most excellent things concerning deep learning is that it assists you to match the relationship between two entities e.g., x and y and enables you to do magnificent things like image classification into different categories. Your task is to make a Solution to classify the images in the dateset belonging to their respective categories.
The Fashion-MNIST serves as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. It consists of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
- 0 T-shirt/top
- 1 Trouser
- 2 Pullover
- 3 Dress
- 4 Coat
- 5 Sandal
- 6 Shirt
- 7 Sneaker
- 8 Bag
- 9 Ankle boot
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747
The dateset includes two training files and one test directory namely:
- test directory
which contains training images and training labels respectively. The test directory consists of 10,000 28x28 grayscale images and have to be classified into 10 classes.
The final submission must include:
output.csv (single file having results for "test" directory which contains 10,000 images )
For grading, "output.csv" file must be uploaded in the submission section of this challenge.
The first row should correspond to the column names "filename" and "label".
How to make a submission?
- Click on "My Submission".
- On the next page, click on "+ New Submission".
- Upload your CSV in the next page and click on "Submit for Review".
- You must submit your CSV file by uploading the CSV in the "My Submissions" section of this challenge.
- Your submission will be auto graded and you will be able to see your results instantly.
- If there is any error in the submission, your final score will be marked as 0.
- Accuracy Score
- The submission should be in a proper format as described by "Submission Guidelines".
- Late submission will not be accepted beyond provided deadline (Indian Standard Time).
NOTE: We may request for the code files if there is any discrepancy in your score. Your score will be marked invalid if the request for code is not fulfilled by you.
- Top three participants will get a place in Hall of Fame page.