Universe

ROB6-segmentation_model Computer Vision Dataset

byUniversityTask:
Object Detection
License:CC BY 4.017 views2 downloads
See all 1.3k imagesROB6-segmentation_model Litter sample showing lidROB6-segmentation_model Litter sample showing Pizza containerROB6-segmentation_model Litter sampleROB6-segmentation_model Litter sample showing Pizza containerROB6-segmentation_model Litter sampleROB6-segmentation_model Litter sample showing bottle, can, cup, food packagingROB6-segmentation_model Litter sample showing bottleROB6-segmentation_model Litter sample showing can, flaskROB6-segmentation_model Litter sample showing bottleROB6-segmentation_model Litter sample showing Pizza containerROB6-segmentation_model Litter sampleROB6-segmentation_model Litter sample

About ROB6-segmentation_model Dataset

Here are a few use cases for this project:

  1. Waste Sorting Application: This model could be utilized in a smart waste sorting system to automatically differentiate types of litter and direct them to the appropriate recycling or disposal area, increasing efficiency and accuracy in waste management.

  2. Automated Street Cleaning: This model could be incorporated into a street cleaning robot system where it identifies types of litter on streets, pavements or other public places, making cleaning processes more effective and prioritizing areas based on litter type and quantity.

  3. Environmental Research: The model can be used by environmental researchers for monitoring litter in urban areas or natural habitats to understand usage patterns and human impact. This data can support a wide range of environmental studies and urban planning activities.

  4. Public Awareness and Educational Campaigns: Images segmented using the model could be used to educate the public about various types of common litter and their impact on the environment, prompting more responsible behavior.

  5. Retail Insights: For businesses, the model can provide insights about the most frequently discarded items around their stores. These insights can help them make more conscious decisions regarding product packaging methods.

Use Free Bottle, Cup and Can 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": "bottle, cup, can, flask, food packaging"
  },
  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{ rob6-segmentation_model_dataset,
  title = { ROB6-segmentation_model Dataset },
  type = { Open Source Dataset },
  author = { University },
  howpublished = { \url{ https://universe.roboflow.com/university-vnrwt/rob6-segmentation_model } },
  url = { https://universe.roboflow.com/university-vnrwt/rob6-segmentation_model },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2024 },
  month = { oct },
  note = { visited on 2026-07-29 },
}

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