Universe

FFDR V1 Computer Vision Dataset

byayyappanTask:
Object Detection
License:CC BY 4.012 views1 download
See all 927 imagesFFDR V1 cars sample showing carFFDR V1 cars sample showing carFFDR V1 cars sample showing car, vanFFDR V1 cars sample showing carFFDR V1 cars sample showing traffic lightFFDR V1 cars sampleFFDR V1 cars sample showing carFFDR V1 cars sampleFFDR V1 cars sample showing carFFDR V1 cars sampleFFDR V1 cars sampleFFDR V1 cars sample

About FFDR V1 Dataset

Here are a few use cases for this project:

  1. Autonomous Vehicle Navigation: FFDR V1 could be used to identify, classify, and interpret various objects on the road for self-driving car development. This application can help autonomous vehicles decipher different elements such as cars, traffic lights, stop signs, and pedestrians in real-time, thereby facilitating safe and efficient navigation.

  2. Traffic Management Systems: The model can assist in monitoring and analyzing traffic flow in urban areas, helping authorities make data-driven decision-making for traffic light timing or creating more efficient traffic routes.

  3. Surveillance & Public Safety: FFDR V1 can be deployed in surveillance systems to identify vehicles, analyze traffic patterns, and detect anomalous behavior for crime prevention or investigative purposes. It can also detect pedestrians and cyclists in potential collision scenarios improving public safety.

  4. Augmented Reality (AR) Games: The model can be utilized for creating hyper-realistic AR games that interact with real-world vehicles, bikes, and pedestrians, offering an enhancing gaming experience for users.

  5. Intelligent Transportation Systems: FFDR V1 can serve in optimizing public transport systems by helping count and classify vehicles like buses, trains, motorbikes, etc., thereby aiding in transit planning, route optimization, and performance evaluation.

Use Free Car, Truck and Bus 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, truck, bus, bicycle, bike"
  },
  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{ ffdr-v1_dataset,
  title = { FFDR V1 Dataset },
  type = { Open Source Dataset },
  author = { ayyappan },
  howpublished = { \url{ https://universe.roboflow.com/ayyappan-l9ko4/ffdr-v1 } },
  url = { https://universe.roboflow.com/ayyappan-l9ko4/ffdr-v1 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { feb },
  note = { visited on 2026-07-29 },
}

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