Universe

Screws_detection Computer Vision Dataset

byMatthew PerrymanTask:
Object Detection
License:MIT93 views3 downloads
See all 1.2k imagesScrews_detection Screws sampleScrews_detection Screws sample showing phillipsScrews_detection Screws sample showing phillipsScrews_detection Screws sample showing phillipsScrews_detection Screws sample showing phillipsScrews_detection Screws sample showing phillipsScrews_detection Screws sample showing phillipsScrews_detection Screws sampleScrews_detection Screws sample showing phillipsScrews_detection Screws sample showing phillipsScrews_detection Screws sampleScrews_detection Screws sample

About Screws_detection Dataset

Here are a few use cases for this project:

  1. Industrial Quality Control: This model could be used in automated assembly lines or factories to ensure the correct type of screw is used during the production. It can help in reducing manual errors and optimizing the manufacturing process.

  2. Hardware Retail: In a hardware store, the Screws_detection model could facilitate the quick inventory of different types of screws, thereby improving organization and customer service.

  3. Augmented Reality Applications: This computer vision model could enhance AR apps used in home improvement or construction by accurately identifying screw types, providing the user guides on how to use them appropriately.

  4. Recycling and Waste Management: The model can be used to detect, identify, and sort screws in waste management facilities, aiding in the recycling process and segregating these components from general waste.

  5. Education and Training: It could be utilized in online learning platforms or industrial training sessions, to help students or trainees learn and differentiate between various types of screws.

Use Free Phillips 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": "phillips"
  },
  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

LicenseMIT

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ screws_detection_dataset,
  title = { Screws_detection Dataset },
  type = { Open Source Dataset },
  author = { Matthew Perryman },
  howpublished = { \url{ https://universe.roboflow.com/matthew-perryman/screws_detection } },
  url = { https://universe.roboflow.com/matthew-perryman/screws_detection },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { feb },
  note = { visited on 2026-07-29 },
}

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