Robust Shelf Monitoring Computer Vision Project

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Robust Shelf Monitoring

We aim to build a Robust Shelf Monitoring system to help store keepers to maintain accurate inventory details, to re-stock items efficiently and on-time and to tackle the problem of misplaced items where an item is accidentally placed at a different location. Our product aims to serve as store manager that alerts the owner about items that needs re-stocking and misplaced items.

Training the model:

  • Unzip the labelled dataset from kaggle and store it to your google drive.
  • Follow the tutorial and update the training parameters in custom-yolov4-detector.cfg file in /darknet/cfg/ directory.
  • filters = (number of classes + 5) * 3 for each yolo layer.
  • max_batches = (number of classes) * 2000

Steps to run the prediction colab notebook:

  1. Install the required dependencies; pymongo,dnspython.
  2. Clone the darknet repository and the required python scripts.
  3. Mount the google drive containing the weight file.
  4. Copy the pre-trained weight file to the yolo content directory.
  5. Run the script to peform the prediction.

Presenting the predicted result.

The script have option to send SMS notification to the shop keepers. We have built a front-end for building the phone-book for collecting the details of the shopkeepers. It also displays the latest prediction result and model accuracy.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ robust-shelf-monitoring_dataset,
    title = { Robust Shelf Monitoring Dataset },
    type = { Open Source Dataset },
    author = { Shelf Monitoring },
    howpublished = { \url{ } },
    url = { },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { oct },
    note = { visited on 2022-12-04 },

Last Updated

2 months ago

Project Type

Object Detection




ariel, boost, ghee, harpic, oil, pickle, tea


CC BY 4.0

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