All Tracks Problem

Identify the product category



Problem Statement

The applications of Deep Neural Nets is on a roll. Whether it is healthcare, transportation, or retail, companies across all industries are excited about investing in building intelligent solutions. Meanwhile, let’s hope human intelligence remains uncontested.

In this challenge, you will help one of the largest retailers in Germany improve their inventory-management process in its Food and Groceries business. The company is looking for intelligent solutions that can reduce the amount of human effort in its warehouse and retail outlets.

A solution such as a powerful image classifier can help the company track shelf inventory, categorize products, record product volume etc.

You are required to predict the category of each product.

Download Dataset

Data description

The zipped file contains images of training and testing set. The train data has 3215 product images. The test data has 1732 product images.

Variable Description
image_id unique id of image
label product category (target)


A participant has to submit a .csv file containing the image_id and labels in the .csv format. Check the sample submission file for the format.

test_1000a, candy
test_1000b, water
test_1000c, coffee
test_1000d, water
test_1001a, rice

Evaluation metric

Each submission will be evaluated based on weighted F1 score. Higher the better. To know more, read here.



  • [Sep 04, 17:54] Kindly make sure your pre-trained models are publicly available.
  • [Sep 14, 18:00] For all your IBM DSX related queries, please ask on this slack channel: Slack Channel. You can get your slack invite here.
Time Limit: 5.0 sec(s) for each input file.
Memory Limit: 256 MB
Source Limit: 1024 KB
Marking Scheme: Marks are awarded when all the testcases pass.
Allowed Languages: C, C++, C++14, Clojure, C#, D, Erlang, F#, Go, Groovy, Haskell, Java, Java 8, JavaScript(Rhino), JavaScript(Node.js), Julia, Kotlin, Lisp, Lisp (SBCL), Lua, Objective-C, OCaml, Octave, Pascal, Perl, PHP, Python, Python 3, R(RScript), Racket, Ruby, Rust, Scala, Swift, Visual Basic
Upload Prediction File
Please upload the prediction file in the format as stated in the problem and ensure that there are correct number of rows as in the test file.
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