Universe

second-data Computer Vision Dataset

bycola new verTask:
Object Detection
License:CC BY 4.029 views2 downloads
See all 6k imagessecond-data cola sample showing colasecond-data cola samplesecond-data cola sample showing fantasecond-data cola sample showing colasecond-data cola sample showing fantasecond-data cola sample showing fanta, spritesecond-data cola samplesecond-data cola sample showing fantasecond-data cola sample showing spritesecond-data cola sample showing fantasecond-data cola sample showing spritesecond-data cola sample showing cola

About second-data Dataset

Here are a few use cases for this project:

  1. Use Case "Stock Management": In retail environments, the "second-data" model can be utilized to monitor the stock levels of different drink brands, such as Cola, Fanta, and Sprite, in real-time. By identifying product on shelves, it helps in instant restocking based on demand, automates inventory management, and can also assist in setting up promotional campaigns for specific beverages.

  2. Use Case "Recycling Initiatives": It can facilitate recycling initiatives by distinguishing between varying drink cans/bottles, supporting automated waste sorting, and ensuring each bottle/can is properly recycled based on its class. This can be particularly useful in waste management facilities or recycling schemes.

  3. Use Case "Automated Vending Machines": Vending machines can incorporate this model to automatically identify and dispense the selected soda brand by visual recognition rather than traditional code-based systems.

  4. Use Case "Consumer Behavior Research": Study consumer behavior in supermarkets or convenience stores. Using CCTV footage, the model can identify which soda brands consumers are interacting with the most, providing valuable insights for marketing and sales strategies.

  5. Use Case "Autonomous Retail Checkout": This model can be implemented in autonomous checkout systems to identify the purchased soda brand. As customers add products to their cart, the system would identify them, adding the appropriate price to their running total, and making checkout seamless and quick.

Use Free Pizza, Sprite and Burger 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": "pizza, sprite, burger, cola, doner"
  },
  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{ second-data_dataset,
  title = { second-data Dataset },
  type = { Open Source Dataset },
  author = { cola new ver },
  howpublished = { \url{ https://universe.roboflow.com/cola-new-ver/second-data } },
  url = { https://universe.roboflow.com/cola-new-ver/second-data },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { nov },
  note = { visited on 2026-07-29 },
}

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