Universe

tassy_Bruvs Computer Vision Dataset

byBen MaslenTask:
Object Detection
License:CC BY 4.013 views1 download
See all 1.9k imagestassy_Bruvs Fish sampletassy_Bruvs Fish sampletassy_Bruvs Fish sampletassy_Bruvs Fish sample showing Platycephalus bassensistassy_Bruvs Fish sampletassy_Bruvs Fish sample showing Cephaloscyllium laticeps, Diogenidae, Platycephalus bassensistassy_Bruvs Fish sampletassy_Bruvs Fish sample showing Cephaloscyllium laticepstassy_Bruvs Fish sampletassy_Bruvs Fish sampletassy_Bruvs Fish sample showing Meuschenia australistassy_Bruvs Fish sample showing Cephaloscyllium laticeps, Platycephalus bassensis

About tassy_Bruvs Dataset

Here are a few use cases for this project:

  1. Marine Biodiversity Research: Researchers studying marine biodiversity can use the "tassy_Bruvs" model to identify and categorize different fish species captured on underwater cameras, which can provide a comprehensive understanding of the different fish ecosystems.

  2. Marine Conservation Efforts: Conservationists can use this model to monitor different fish populations, track endangered species, and study the impacts of climate change or human interference on marine ecosystems.

  3. Commercial Fishing: The model can be used to identify bycatch (unwanted fish species caught during commercial fishing) in real-time and thus help fishers to significantly reduce bycatch and maintain a more sustainable fishing practice.

  4. Aquarium Management: The model can aid in maintaining diverse species populations in an aquarium by helping to identify and monitor the health of different fish species.

  5. Educational Tools: The model can be used in creating interactive and educational software for students studying marine biology or zoology, providing visual examples of different types of fish and their characteristics.

Use Free Arripis truttaceus, Brachaluteres jacksonianus and Carangidae 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": "Arripis truttaceus, Brachaluteres jacksonianus, Carangidae, Cephaloscyllium laticeps, Chyrosophyrs auratus"
  },
  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{ tassy_bruvs_dataset,
  title = { tassy_Bruvs Dataset },
  type = { Open Source Dataset },
  author = { Ben Maslen },
  howpublished = { \url{ https://universe.roboflow.com/ben-maslen/tassy_bruvs } },
  url = { https://universe.roboflow.com/ben-maslen/tassy_bruvs },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2024 },
  month = { may },
  note = { visited on 2026-07-29 },
}

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