Universe

vega Computer Vision Dataset

byEnzio VelazqueoniTask:
Object Detection
License:CC BY 4.022 views1 download
See all 357 imagesvega lifts sample showing Lift, Streakvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Liftvega lifts sample showing Lift, Streakvega lifts sample showing Liftvega lifts sample showing Lift

About vega Dataset

Here are a few use cases for this project:

  1. Elevator Maintenance: The "Vega" computer vision model can be used by technicians to identify and classify different types of lifts or lift parts, such as lifts and streaks, during regular maintenance. By recognizing specific aspects, it can help predict potential issues and improve preventive measures.

  2. Construction and Architecture: The model can be used to correctly identify lifts and streaks in the construction of new buildings. This information can assist in the planning phase, ensuring that the right equipment is installed correctly.

  3. Accessibility Analysis: The model can help in analyzing environments for accessibility. It can scan an area and identify where lifts are located, helping to determine how accessible a place is for individuals with physical disabilities.

  4. Warehouse Management: The "Vega" model can be employed to identify and track the usage of lifts in warehouses. Recognizing distinct usage patterns can help in optimizing lift usage and warehouse operations.

  5. Real Estate Industry: It can be used to quickly evaluate and catalogue the types and conditions of lifts in residential or commercial properties. This could aid in property assessment and inspection processes.

Use Free Lift and Streak 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": "Lift, Streak"
  },
  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{ vega_dataset,
  title = { vega Dataset },
  type = { Open Source Dataset },
  author = { Enzio Velazqueoni },
  howpublished = { \url{ https://universe.roboflow.com/enzio-velazqueoni/vega } },
  url = { https://universe.roboflow.com/enzio-velazqueoni/vega },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { mar },
  note = { visited on 2026-07-29 },
}

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