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Top Inventory Datasets
Open source inventory computer vision datasets, pre-trained models, and APIs.
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.
- 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
- 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.
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.
This project was created after collecting images from retail coolers at Walgreens stores in Chicago, Illinois and Iowa City, Iowa.
All products are marked/labeled as
product, and all empty spaces are labeled as
This is a good starter-dataset for anyone interested in doing "void space" calculations (percent of space that is empty), product inventory counts, or making more custom classes for individual product inventory counts.
Versions 2 and 3 were trained from the Microsoft COCO model checkpoint.
For more, check out the blog posts below:
- This dataset enjoys the following characteristics: (1) It is by far the largest dataset in terms of both product image quantity and product categories. (2) It includes single-product images taken in a controlled environment and multi-product images taken by the checkout system. (3) It provides different levels of annotations for the checkout images. Comparing with the existing datasets, ours is closer to the realistic setting and can derive a variety of research problems.
This dataset could be used to create an automatic item counter or checkout system using computer vision with Roboflow's API, Python Package, or other deployment options, such as Web Browser, iOS device, or to an Edge Device: https://docs.roboflow.com/inference/hosted-api.
Using this Dataset
This dataset has been licensed on a CC BY 4.0 license. You can copy, redistribute, and modify the images as long as there is appropriate credit to the authors of the dataset.
Roboflow creates tools that make computer vision easy to use for any developer, even if you're not a machine learning expert. You can use it to organize, label, inspect, convert, and export your image datasets. And even to train and deploy computer vision models with no code required.
Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark