Universe

AUTOHOLO_v5 Computer Vision Dataset

byDelaney SanbornTask:
Object Detection
License:CC BY 4.037 views3 downloads
See all 3.1k imagesAUTOHOLO_v5 sturgeon-minnow sample showing sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing null, sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing minnowAUTOHOLO_v5 sturgeon-minnow sample showing 0, 1AUTOHOLO_v5 sturgeon-minnow sample showing null, sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing sturgeonAUTOHOLO_v5 sturgeon-minnow sample showing 0, 1AUTOHOLO_v5 sturgeon-minnow sample showing 0, 1, nullAUTOHOLO_v5 sturgeon-minnow sample showing sturgeon

About AUTOHOLO_v5 Dataset

Here are a few use cases for this project:

  1. Fish farming management: AUTOHOLO_v5 can be used in aquaculture facilities to monitor and differentiate between sturgeon and minnow populations, streamlining the sorting process and ensuring accurate stocking rates for efficient production.

  2. Wildlife conservation and ecological research: Researchers can utilize AUTOHOLO_v5 to monitor and study sturgeon-minnow classes in their natural habitats, helping them assess biodiversity, population dynamics, and overall ecosystem health.

  3. Environmental impact assessments: Agencies can use the model to analyze the effects of developmental projects or pollution events on sturgeon and minnow populations, facilitating timely and precise mitigation measures.

  4. Fishing industry: Both commercial and recreational fishermen could benefit from AUTOHOLO_v5 by identifying areas with high sturgeon and minnow populations, optimizing fishing efforts and ensuring adherence to catch limits and regulations.

  5. Education and awareness: AUTOHOLO_v5 can be incorporated into educational materials, such as interactive exhibits in natural history museums and outreach programs, aimed at raising awareness about aquatic species and the importance of preserving biodiversity in water ecosystems.

Use Free Bubble, 0 and 1 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": "bubble, 0, 1, minnow, null"
  },
  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{ autoholo_v5_dataset,
  title = { AUTOHOLO_v5 Dataset },
  type = { Open Source Dataset },
  author = { Delaney Sanborn },
  howpublished = { \url{ https://universe.roboflow.com/delaney-sanborn/autoholo_v5 } },
  url = { https://universe.roboflow.com/delaney-sanborn/autoholo_v5 },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { feb },
  note = { visited on 2026-07-29 },
}

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