Cereal box identification in store shelves using computer vision and a single train image per model.

Last update: Jan 21, 2022

Overview

Product Recognition on Store Shelves

Description

You can read the task description here.

Report

You can read and download our report here.

Step A - Multiple Product Detection

See the results in this jupyter notebook. Or download and run it!

Step B - Multiple Instance Detection

See the results in this jupyter notebook. Or download and run it!

Step C - Whole shelve challenge

See the results in this jupyter notebook. Or download and run it!

Packages versions

cv2: 4.4.0

numpy: 1.19.2

matplotlib: 3.3.2

Extra

(Optional) If you want to visualize additional information such as plots of intermediate steps of the whole process, then download and run this jupyter notebook.

Team

Owner

Nicholas Baraghini

MSC Automation Engineer Student presso l'Università di Bologna | 🐉Dragon Rider | Curious Learner

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Cereal box identification in store shelves using computer vision and a single train image per model.

Related tags

Overview

Product Recognition on Store Shelves

Description

Report

Step A - Multiple Product Detection

Step B - Multiple Instance Detection

Step C - Whole shelve challenge

Packages versions

Extra

Team

Owner

Nicholas Baraghini

MMFlow is an open source optical flow toolbox based on PyTorch

Constrained Logistic Regression - How to apply specific constraints to logistic regression's coefficients

Using VapourSynth with super resolution models and speeding them up with TensorRT.

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PyTorch implementation of "A Two-Stage End-to-End System for Speech-in-Noise Hearing Aid Processing"

Source code of the paper "Deep Learning of Latent Variable Models for Industrial Process Monitoring".

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PyTorch implementation of neural style transfer algorithm

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