Universe

first-data 2 Computer Vision Model

bycola new verTask:
Object Detection
License:Public Domain14 views1 download
See all 1.8k imagesfirst-data 2 cola-fanta-sprite-5J8D sample showing fantafirst-data 2 cola-fanta-sprite-5J8D sample showing spritefirst-data 2 cola-fanta-sprite-5J8D sample showing cola, fantafirst-data 2 cola-fanta-sprite-5J8D sample showing spritefirst-data 2 cola-fanta-sprite-5J8D sample showing fantafirst-data 2 cola-fanta-sprite-5J8D sample showing fantafirst-data 2 cola-fanta-sprite-5J8D sample showing spritefirst-data 2 cola-fanta-sprite-5J8D sample showing colafirst-data 2 cola-fanta-sprite-5J8D samplefirst-data 2 cola-fanta-sprite-5J8D sample showing spritefirst-data 2 cola-fanta-sprite-5J8D sample showing colafirst-data 2 cola-fanta-sprite-5J8D sample showing cola

About first-data 2 Model

Here are a few use cases for this project:

  1. Automated Vending Machine Inventory: The "first-data" model can be used to keep track of inventory in vending machines. For instance, an AI system could periodically scan the arrangement and quantity of the drinks to identify whether the machine is out of stock of Sprite, Cola, or Fanta.

  2. Intelligent Recycling Bins: Computer vision can be deployed in smart recycling bins to identify the type of drink containers being discarded. By distinguishing between Sprite, Cola, and Fanta cans or bottles, the system can provide more precise data for recycling studies, or apply specific recycling processes.

  3. Brand Market Analysis: In a retail environment, insights about the popularity of different drink brands can be drawn by using security footage to identify the purchase of Sprite, Cola, or Fanta drinks.

  4. Advertising Efficacy Metrics: Brands can use such a model in assessing the effectiveness of their in-store advertising or placements by using security cameras to analyze the selection preference of customers for Sprite, Cola, and Fanta.

  5. Health and Nutrition Studies: The model can be used in nutritional studies to track the consumption of different types of sodas. This data could be utilized to understand people's drinking habits and to plan better public health policies or interventions.

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

LicensePublic Domain

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

@misc{ first-data-2_dataset,
  title = { first-data 2 Dataset },
  type = { Open Source Dataset },
  author = { cola new ver },
  howpublished = { \url{ https://universe.roboflow.com/cola-new-ver/first-data-2 } },
  url = { https://universe.roboflow.com/cola-new-ver/first-data-2 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { nov },
  note = { visited on 2026-07-29 },
}

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