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 detect.py script 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.

This dataset was originally created by Anonymous. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/food7/test1-iajnv.

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: https://github.com/roboflow-ai/roboflow-100-benchmark

Here are a few use cases for this project:

1. Inventory Management: Retailers can use the "Retail Coolers" model to monitor and manage their inventory by keeping track of stocked and empty spaces within the cooler. This will streamline the process of replenishment, reducing out-of-stock events, and improving overall customer experience.

2. Sales Analysis: Businesses can analyze customers' purchasing behavior using the "Retail Coolers" model to identify fast-moving or slow-moving products within coolers. This information can guide pricing, promotions, and product placement strategies to optimize sales and profit margins.

3. Automated Restocking Alerts: The "Retail Coolers" model can trigger automatic notifications to store staff or delivery partners when it detects empty spaces in the cooler. This will ensure timely restocking, ultimately improving customers' shopping experience and the store's revenue generation.

4. Space Optimization: The "Retail Coolers" model can help retailers optimize the use of cooler spaces by identifying popular products that frequently run out or empty spots. Data-driven insights can guide store layouts and product arrangements to maximize sales and cooler efficiency.

5. Customer Behavior Insights: By analyzing changes in cooler stock over time, businesses can gain insight into customer behavior, preferences, and consumption patterns. This information can guide targeted marketing, sales strategies, and category management to better serve customers and improve overall store performance.

Here are a few use cases for this project:

1. Inventory Management: With the use of this model, retail managers could dynamically track product availability on supermarket shelves. By identifying which sections are empty, they can streamline restocking processes.

2. Customer Shopping Experience Improvement: The model could be used within a smartphone application to guide customers directly to stocked items they are searching for, and inform them in real-time if any items are out of stock.

3. Predictive Analytics for Supply Chain: By integrating this model with sales and supply chain data, it could assist in predicting future missing stock scenarios, contributing to smoother supply chain operations and avoiding customer disappointments.

4. Shelf Space Optimization: The model can be used by store managers to understand consumer behavior and optimize shelf space by recognizing patterns in which products are often missing from shelves.

5. Automated Checkout Systems: Integrating this model within an automated checkout system, the system could recognize when a product has been taken off the shelf and add it immediately to the customer's virtual basket, creating an efficient checkout process.

Here are a few use cases for this project:

1. Retail Inventory Management: The "Sale Detection" model can be used in stores to automate tracking of ongoing sales and discounts in real time, enabling store managers to monitor inventory levels and re-stock items more efficiently.

2. Shopping Assistant Apps: The model can be integrated into shopping apps that help consumers find the best deals and discounts on products by scanning shelves or store displays and detecting sale signs or price reductions.

3. Digital Marketing & Advertising Analysis: The "Sale Detection" model can be used by marketing professionals to analyze the effectiveness of various sales promotions based on customer responses, helping them optimize future marketing campaigns.

4. Dynamic Pricing and Revenue Optimization: E-commerce platforms and online retailers can use the "Sale Detection" model to analyze competitor prices and promotions, allowing them to adjust their own prices and offers dynamically to remain competitive.

5. Customer Behavior Analysis: The model can be used to analyze in-store customer behavior and engagement, such as how much time customers spend in specific sale areas, which sales tactics attract more attention, and what types of sales promotions generate the most customer interactions.

Overview

Via https://rpc-dataset.github.io:

• 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: 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.

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.

Here are a few use cases for this project:

1. Retail Inventory Management: The "and-qrs" model could be used in retail settings to quickly scan and identify items in stock based on their barcodes or QR codes. This can greatly improve speed and efficiency in inventory management.

2. Package Tracking: Logistic firms can use the model to scan barcodes or QR codes on packages, aiding in real-time tracking and sorting of parcels, helping to streamline delivery processes.

3. Health and Safety Compliance: In industries that require health and safety compliance, the model can be used to quickly scan and verify barcodes on hazardous materials or safety equipment, ensuring they meet legal requirements.

4. Access Control: The model could be deployed in access control systems, using barcodes or QR codes for identification and authorization. This use case can be applicable for offices, conferences, or events.

5. Food Safety: It can be used in supermarkets or grocery stores to scan barcodes on food items. This can help identify product details including ingredients, nutritional facts or allergen information, helping customers make informed choices.