Universe

Morts Computer Vision Dataset

byRowan mohamedTask:
Object Detection
License:CC BY 4.023 views
See all 2.8k imagesMorts dead-fish sampleMorts dead-fish sample showing mortMorts dead-fish sampleMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mortMorts dead-fish sample showing mort

About Morts Dataset

Here are a few use cases for this project:

  1. Environmental Monitoring: Morts can be used by researchers and environmental agencies to assess the health of aquatic ecosystems by scanning bodies of water for dead fish and determining the species, providing valuable data to track and prevent fish mortality events.

  2. Fishery Management: Fisheries and fish farms can employ the Morts model to monitor their stocks better and enhance their management practices, enabling them to identify and remove dead fish more effectively, maintain higher water quality, and optimize their production.

  3. Water Treatment Plants: Morts can be a beneficial tool for water treatment plants to evaluate the efficacy of their waste removal and water purification processes by assessing the presence of dead fish in water streams before and after the treatment.

  4. Wildlife Conservation: Conservation organizations can use the Morts model in their efforts to protect and restore aquatic habitats by identifying dead fish in crucial breeding grounds, which can serve as an indicator of habitat degradation, pollution, and the effectiveness of their conservation measures.

  5. Monitoring Aquatic Disaster: Morts can be used to assess the impact of natural disasters, oil spills, or pollution events on aquatic life by providing a quick and accurate estimation of the number of dead fishes, their species, and the affected areas, thereby helping in effective disaster management and mitigation planning.

Use Free Mort 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": "mort"
  },
  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{ morts_dataset,
  title = { Morts Dataset },
  type = { Open Source Dataset },
  author = { Rowan mohamed },
  howpublished = { \url{ https://universe.roboflow.com/rowan-mohamed/morts } },
  url = { https://universe.roboflow.com/rowan-mohamed/morts },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { may },
  note = { visited on 2026-07-29 },
}

Similar Projects

See More