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Top Retail Item Detection Datasets

Roboflow hosts the world's biggest set of open-source retail store item datasets and pre-trained computer vision models. Images are captured from personal cameras, mobile phones, and more. The datasets include collections from other popular open-source projects, such as SKU-110k, these projects can help you find objects of interest in retail store photos and videos.

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.

Nike, Adidas and Converse Shoes Dataset for Classification

This dataset was obtained from Kaggle: https://www.kaggle.com/datasets/die9origephit/nike-adidas-and-converse-imaged/

Dataset Collection Methodology:

"The dataset was obtained downloading images from Google images. The images with a .webp format were transformed into .jpg images. The obtained images were randomly shuffled and resized so that all the images had a resolution of 240x240 pixels. Then, they were split into train and test datasets and saved."

Versions:

  • v1: original_raw-images: the original images without Preprocessing or Augmentation applied, other than Auto-Orient to remove EXIF data. These images are in the original train/test split from Kaggle: 237 images in each train set and 38 images in each test set
  • v2: original_trainTestSplit-augmented3x: the original train/test split, augmented with 3x image generation. This version was not trained with Roboflow Train.
  • v3: original_trainTestSplit-augmented5x: the original train/test split, augmented with 5x image generation. This version was not trained with Roboflow Train.
  • v4: rawImages_70-20-10split: the original images without Preprocessing or Augmentation applied, other than Auto-Orient to remove EXIF data. Dataset splies were modified to a 70% train, 20% valid, 10% test train/valid/test split
    • NOTE: 70%/20%/10% split: 576 images in train set, 166 images in valid set, 83 images in test set
  • v5: 70-20-10split-augmented3x: modified to a 70% train, 20% valid, 10% test train/valid/test split, augmented with 3x image generation. This version was trained with Roboflow Train.
  • v6: 70-20-10split-augmented5x: modified to a 70% train, 20% valid, 10% test train/valid/test split, augmented with 5x image generation. This version was trained with Roboflow Train.

Indoor Scene Recognition

Examples of Images
From the official dataset page:
Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information.

Database

The database contains 67 Indoor categories ... The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only.

Paper

A. Quattoni, and A.Torralba. Recognizing Indoor Scenes. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.

Acknowledgments

Thanks to Aude Oliva for helping to create the database of indoor scenes.
                            Funding for this research was provided by NSF Career award (IIS 0747120)
                            

This projects combines the Dollar Bill Detection project from Alex Hyams (v13 of the project was exported in COCO JSON format for import to this project) and the Final Counter, or Coin Counter, project from Dawson Mcgee (v6 of the project was exported in COCO JSON format for import to this project).

v1 contains the original imported images, without augmentations. This is the version to download and import to your own project if you'd like to add your own augmentations.

This dataset can be used to create computer vision applications in the banking and finance industry for use cases like detecting and counting US currency.

v12 contains the original, raw images, with annotations. It includes the following classes:

  • one-front, one-back, five-front, five-back, ten-front, ten-back, twenty-front, twenty-back, fifty-front, fifty-back

v13 contains the original, raw images, with annotations and Modified Classes. It includes the following classes:

  • one, five, ten, twenty, fifty

Wecome!

This is a project on training the machine to read and pickup wine label information, specifically there are several class labels I look at from each of the wine labels, in each class, specific class attributes (such as under the wine type different attributes: Cabernet Sauvignion or Riesling or Merlot) can be assigned to provide more detailed information:

(1)Maker/Name of the vineyard or producer
(2)Vintage/Year of the wine produced
(3)Whether being sustainable or sustainably farmed
(4)Whether being organic or not
(5)Alcohol level
(6)Appellation Quality in terms of common AVA ratings
(7)Established Year of the vineyard
(8)Whether having any appelation AOC DOC AVA name
(9)Whether Country of the origin can be identified
(10)Whether type of the wine can be identified
(11)Whether there is distinct picture or brand logo
(12) Whether there is indication of sweetness level

I hope we all can help train the machine to be better at reading the wine label and be smarter and make more quality inference rather than just reading and picking up information as it which would be just like an OCR

-Yilong "Eric" Zheng

This dataset was originally created by Minoj Selvaraj. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/minoj-selvaraj/furniture-sfocl.

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

This dataset was originally created by Alex Hyams. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/alex-hyams-cosqx/cash-counter/.

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

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 Walmart's 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

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