Universe

Venom Computer Vision Dataset

byDOCKTask:
Object Detection
License:CC BY 4.0
See all 4.1k imagesVenom Venom sample showing big bus, big truck, car, small truckVenom Venom sample showing car, truck(l), truck(s)Venom Venom sample showing car, small truck, truck(l), truck(m)Venom Venom sample showing car, truck(l), truck(m), truck(s), truck(xl)Venom Venom sample showing big truck, car, small busVenom Venom sample showing carVenom Venom sample showing big truck, car, small truckVenom Venom sample showing bus(s), car, small truck, truck(l), truck(m), truck(xl)Venom Venom sample showing big truck, car, truck(m), truck(s)Venom Venom sample showing bus(l), carVenom Venom sample showing big bus, car, small truck, truck(l), truck(m)Venom Venom sample showing big truck, car, small truck

About Venom Dataset

Here are a few use cases for this project:

  1. Traffic Management and Analysis: The "Venom" model can be used by city planners or traffic management authorities to analyze the flow of different types of vehicles on roads. This can help in the development of better transportation infrastructure, efficient traffic signals, and congestion-free roads.

  2. Fleet Management: Companies managing fleets of vehicles can use the "Venom" model to categorize their fleets based on vehicle size and type. This can help in efficient route planning, vehicle maintenance, and cost optimization.

  3. Parking Space Optimization: Parking lot owners can use the "Venom" model to identify available spaces that are appropriate for different types of vehicles, allowing for the optimal utilization of parking spaces.

  4. Accident Investigation and Reconstruction: Law enforcement agencies can use the "Venom" model to identify and classify vehicles involved in road accidents. This can help in better understanding and reconstructing accident scenes, as well as providing valuable input for legal and insurance purposes.

  5. Augmented Reality Navigation: Developers can integrate the "Venom" model into augmented reality navigation systems, providing drivers with real-time information about surrounding vehicles, including their size and type. This can help in enhancing driver awareness and improving road safety.

Use Free Car, Big bus and Big truck 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": "car, big bus, big truck, bus(l), bus(s)"
  },
  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{ venom-irvk8_dataset,
  title = { Venom Dataset },
  type = { Open Source Dataset },
  author = { DOCK },
  howpublished = { \url{ https://universe.roboflow.com/dock-vdcx1/venom-irvk8 } },
  url = { https://universe.roboflow.com/dock-vdcx1/venom-irvk8 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2025 },
  month = { sep },
  note = { visited on 2026-07-29 },
}

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