Introduction to the problem
Following the introduction of computer vision to the world of retail a new set challenges emerged, such as the detection of products in crowded store displays, fine-grained classification of many visually similar classes, as well as dynamically adapting to changes in data in terms of class appearance variation over time, and new classes that may appear in the images before they are labeled in the dataset.
The task
We would like you to implement a solution for product verification in retail stores. Specifically,
given a set of product spaces each containing some product images, write a solution to alarm a store operator whether a product is kept in its correct product space or not. A product that doesn’t belong to the correct product space is called a Plug.
The dataset contains:
• embeddings.csv: contains embedding vectors [1280x1] of all product images.
• product_spaces.csv: contains product space info for each embedding vector.
• plug_labels.csv: contains plug label for each embedding vector.
The plug_label csv file contains binary labels. Label 0 means that the product belongs to the correct product space and label 1 means that the product is a plug - the product doesn’t belong to the correct product space.
Donload datasets: here
The report
• We are not looking for an approach that gives 100% accuracy, we want you to demonstrate your computer vision and machine learning skills. If your approach doesn’t work, please document what you tried and explain why it didn’t work.
• Please send your coded solution along with a short report explaining your approach (overview of the approach, features chosen, extra images, which classifier and what parameters were used for training), instructions on how to run you work and interpretation of the results. We recommend you use Jupyter notebook for this.
• We encourage and advise all candidates to use Python for the task. Please try to provide code that runs independently of the platform you are using (we will be testing your code on a Linux machine). We will review your work based on your approach and code quality.
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