Universe

shelves Computer Vision Dataset

byMarwansworkTask:
Object Detection
License:CC BY 4.0
See all 2.8k imagesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelvesshelves shelves sample showing shelves

About shelves Dataset

Here are a few use cases for this project:

  1. Retail Inventory Management: The "shelves" model can be used to monitor and track inventory levels in retail stores by recognizing products on the shelves and alerting staff when restocking is needed or when there's an issue with product placement.

  2. Automated Warehouse Organizing: In warehouses or distribution centers, the "shelves" model can help optimize the organization and storage of products by identifying shelf types that are suitable for each item, making order fulfillment more efficient.

  3. Shelf Space Optimization: Retailers can utilize the "shelves" model to analyze shelf space usage and product arrangement, enabling them to optimize product display and improve sales performance by identifying high-performing products that should be given more shelf exposure.

  4. Shelf Maintenance in Libraries: The "shelves" model can be employed in libraries and bookstores to detect and categorize shelves according to their usage, helping librarians maintain order and ensure that books are correctly returned to their designated shelves.

  5. Accessibility Compliance: The "shelves" model can be used to evaluate and identify non-compliant shelving designs in public spaces or commercial establishments, ensuring that they adhere to accessibility standards and providing guidance for necessary adjustments.

Use Free Shelves Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "shelves"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ shelves-ugxt3-uwhpu_dataset,
  title = { shelves Dataset },
  type = { Open Source Dataset },
  author = { Marwanswork },
  howpublished = { \url{ https://universe.roboflow.com/marwanswork/shelves-ugxt3-uwhpu } },
  url = { https://universe.roboflow.com/marwanswork/shelves-ugxt3-uwhpu },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2025 },
  month = { jun },
  note = { visited on 2026-07-29 },
}

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