Universe

Market_ceva Computer Vision Model

bySVACevaTask:
Object Detection
License:CC BY 4.0183 views10 downloads

How to use the Market_ceva Detection API

Try This Model

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Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="market_ceva/1")
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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": "cutii, mancare, oameni, plante, reclame"
  },
  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 
See all 326 imagesMarket_ceva objects sample showing oameni, planteMarket_ceva objects sample showing reclameMarket_ceva objects sample showing oameni, planteMarket_ceva objects sample showing oameni, reclameMarket_ceva objects sample showing mancareMarket_ceva objects sample showing cutii, mancare, oameni, reclameMarket_ceva objects sample showing mancare, oameni, reclameMarket_ceva objects sample showing oameniMarket_ceva objects sample showing cutii, mancareMarket_ceva objects sample showing cutii, oameniMarket_ceva objects sample showing cutii, mancare, reclameMarket_ceva objects sample showing oameni, reclame

About Market_ceva Model

Here are a few use cases for this project:

  1. Retail Analysis: The "Market_ceva" model can be used by retailers to analyze the patterns of people's movement through the store, their interactions with products (mancare, cutii), their engagement with in-store adverts (reclame), and how they relate to store layout including plants (plante). Such valuable insights can help improve product placement and store design.

  2. Smart Surveillance Systems: The model can help monitor public spaces or shops by efficiently identifying human activity, distinguishing between people (oameni), food items (mancare), boxes (cutii), plants (plante), and advertisements (reclame). This information can be used to help improve security and prevent theft.

  3. Outdoor Advertising Effectiveness Evaluation: Companies can use the model to evaluate the effectiveness of their outdoor advertising campaigns (reclame) by analyzing and estimating the number of people who notice their ads.

  4. Urban Planning and Development: The model could be aids urban planners and architects by providing insights about how people interact with urban spaces, including pedestrian movements, areas where people are frequently eating (mancare), popular advertising spaces (reclame), and aesthetic preferences such as the placement of plants (plante) and boxes (cutii).

  5. Crowd Management: In densely populated urban areas or during events, the "Market_ceva" model could assist in managing crowds, identifying emergent situations, detecting anomalies, and ensuring safety rules are followed.

Cite This Project

LicenseCC BY 4.0

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

@misc{ market_ceva_dataset,
  title = { Market_ceva Dataset },
  type = { Open Source Dataset },
  author = { SVACeva },
  howpublished = { \url{ https://universe.roboflow.com/svaceva/market_ceva } },
  url = { https://universe.roboflow.com/svaceva/market_ceva },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { may },
  note = { visited on 2026-07-29 },
}

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