Robust Shelf Monitoring Computer Vision Project
Updated 13 days ago
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.cfgfile in /darknet/cfg/ directory. filters = (number of classes + 5) * 3for each yolo layer.max_batches = (number of classes) * 2000
Steps to run the prediction colab notebook:
- Install the required dependencies; pymongo,dnspython.
- Clone the darknet repository and the required python scripts.
- Mount the google drive containing the weight file.
- Copy the pre-trained weight file to the yolo content directory.
- Run the
detect.pyscript to peform the prediction.
Presenting the predicted result.
The detect.py 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.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
robust-shelf-monitoring-ecmde_dataset,
title = { Robust Shelf Monitoring Dataset },
type = { Open Source Dataset },
author = { work },
howpublished = { \url{ https://universe.roboflow.com/work-0or1c/robust-shelf-monitoring-ecmde } },
url = { https://universe.roboflow.com/work-0or1c/robust-shelf-monitoring-ecmde },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { dec },
note = { visited on 2024-12-18 },
}