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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.

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.

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 empty

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.

Versions 4 and 5 were trained from the SKU-110k model's training checkpoint, from the Roboflow Universe Retail datasets page.

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.

Use Cases

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:

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.

About Roboflow

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.

This dataset was originally created by Anonymous. To see the current project, which may have been updated since this version, please go here:

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: