Universe

unmaned_retail Computer Vision Dataset

bymeicanTask:
Object Detection
License:CC BY 4.011 views
See all 407 imagesunmaned_retail food sample showing 旺旺仙贝, 纯果乐unmaned_retail food sample showing GEMEZ干脆面, 牛油曲奇饼, 纯果乐unmaned_retail food sample showing 良品铺子酥脆薄饼unmaned_retail food sample showing Knoppers, 纯果乐unmaned_retail food sample showing GEMEZ干脆面, 良品铺子酥脆薄饼unmaned_retail food sample showing Knoppers, 旺旺仙贝unmaned_retail food sample showing 牛油曲奇饼unmaned_retail food sample showing Knoppers, 纯果乐unmaned_retail food sample showing GEMEZ干脆面, 牛油曲奇饼, 纯果乐unmaned_retail food sample showing 良品铺子酥脆薄饼unmaned_retail food sample showing Knoppers, 牛油曲奇饼unmaned_retail food sample showing 旺旺仙贝, 纯果乐

About unmaned_retail Dataset

Here are a few use cases for this project:

  1. Automated Retail Checkout: This model could be used in an “Amazon Go"-like checkout system where users can pick up an item and walk out of the store, with the system automatically detecting the items and billing the customers' account.

  2. Inventory Management: Retailers can use this model to quickly scan their inventory and identify stocks of specific candies and snacks, helping them to manage reordering and overstock scenarios.

  3. Nutrition Apps: The model can be employed in nutrition apps to help users identify food products by taking pictures of them, providing a quick way to find out nutritional information.

  4. Learning Platform: The model can be used in education and learning platforms where students learn about different global food items. By scanning pictures of these food items, the model can provide relevant information.

  5. Smart Vending Machines: Populating vending machines with this model could allow them to identify what products are being selected, facilitating a seamless and checkout-free purchasing experience.

Use Free GEMEZ干脆面, Knoppers and 旺旺仙贝 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": "GEMEZ干脆面, Knoppers, 旺旺仙贝, 牛油曲奇饼, 纯果乐"
  },
  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{ unmaned_retail_dataset,
  title = { unmaned_retail Dataset },
  type = { Open Source Dataset },
  author = { meican },
  howpublished = { \url{ https://universe.roboflow.com/meican/unmaned_retail } },
  url = { https://universe.roboflow.com/meican/unmaned_retail },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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