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Team Cherry. The Kaufland Case. Fast and Accurate Image Classification Architecture for Recognizing Produce in a Real-Life Groceries’ Setting

Our best model (derived from VGG) achieved 99.46% top3 accuracy (90.18% top1) with processing time during training of 0.006 s per image on a single GPU Titan X (200s / epoch with 37 000 images).

The teams vision is for the team members to see where they stand compared to others in terms of ideas and approaches to computer vision and to learn new ideas and approaches from the other team-mates and the mentors.

Therefore the team is pursuing a pure computer vision approach to solving the Kaufland and/or the ReceiptBank cases.

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6 thoughts on “Team Cherry. The Kaufland Case. Fast and Accurate Image Classification Architecture for Recognizing Produce in a Real-Life Groceries’ Setting

  1. 1
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    You’ve presented your solution and test results very well. However you shouldn’t just copy the case description as a business understanding. In this section you need to show that you understand what the problem is, who the users of your solution are, and what they expect to see from the system. Good presentation overall and a very interesting article to read. Well done Team Cherry!!

  2. 0
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    Well done! You should have worked more on the final look of the article. Also import the notebook here, so other people can learn from you work, or give you tips how to improve it.

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      Please note that our team had several beginners so here we approached the problem with aim to learn how to train your own network and learn useful tricks on the way. Therefore we start all the way from linear model :). Think of it as 40h intro.

      here is it https://github.com/valanm/datathon2018-kaufland/blob/master/Pipeline.ipynb but we will need to comment on it first.

      Cleaned notebook with only the final model will follow shortly.

      I always make a sample to test and prototype things, and when I am happy I let the model looks at the whole dataset. I like babysitting my models to make them both fast and accurate. This way I can build faster and more accurate models compared to standard pipelines with from tutorials 🙂

  3. 1
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    Very well written article! I especially liked how you detailed the different approaches, even when they didn’t work out eventually. I find this very important and useful information which is often missing in articles.

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