REEFSCAPE Computer Vision Dataset
About REEFSCAPE Dataset
Discovering vacany, distance, and displacement(chassis) metrics for REEFSCAPE game piece(coral).
Use Free Algae, Coral and Non-vacant-coral 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": "algae, coral, non-vacant-coral, vacant-coral"
},
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
Roboflow Agent
Tell the agent what you want to build.
Cite This Project
LicenseMITIf you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ reefscape-oleq8_dataset,
title = { REEFSCAPE Dataset },
type = { Open Source Dataset },
author = { FRC 2025 },
howpublished = { \url{ https://universe.roboflow.com/frc-2025-fbod7/reefscape-oleq8 } },
url = { https://universe.roboflow.com/frc-2025-fbod7/reefscape-oleq8 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { feb },
note = { visited on 2026-07-29 },
}