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Blue Curved Tubes

Image Recognition for Retail
Business

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Technology

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LabelImg

Jupyter notebook

Google Colab

TensorFlow

Customer

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Project Objective

• Create a model in order to detect products shelve share and quantities.

• The higher the shelve share, the higher the probability of closing sales.

• This measure would give the customer an idea of the amount of products they have visible on

  shelves at a given moment in time.

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Machine learning operations (MLOps) steps

  • Take the pictures (High Quality).

  • Label them using a labelling app called LabelImg.

  • Included pictures of objects at different angles and under different lighting conditions.

  • Started with 100 pictures of each class (The different brands and packages of beer).

  • Trained the algorithm using transfer learning using a pre-trained tensorflow model (SSD MobileNet V2 FPNLite 320x320).

  • Peformed an evaluation on 20% of the pictures obstaining very high precision results upwards of 95% accuracy.

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