Universe

yolov8 Computer Vision Model

byPITTask:
Object Detection
License:CC BY 4.0190 views14 downloads

How to use the yolov8 Detection API

Try This Model

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Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="yolov8-u7rpc/4")
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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": "object, 0, 1, 2, 3"
  },
  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 
See all 7.4k imagesyolov8 nut-part sample showing 0, 3yolov8 nut-part sample showing 0, 2yolov8 nut-part sample showing 0, 1yolov8 nut-part sample showing 0, 1yolov8 nut-part sample showing 0, 2yolov8 nut-part sample showing 0, 1yolov8 nut-part sample showing 0, 1yolov8 nut-part sample showing 0, 3yolov8 nut-part sample showing 1yolov8 nut-part sample showing 0, 3yolov8 nut-part sample showing 0, 1yolov8 nut-part sample

About yolov8 Model

Here are a few use cases for this project:

  1. Quality Control in Manufacturing: This model could be used to automatically identify and categorize different nuts and parts on a production line, thereby improving efficiency and reducing instances of human error.

  2. Automated Sorting Systems: The yolov8 model can be employed in automated sorting or recycling facilities to distinguish between different nut classes and objects, promoting efficient material separation.

  3. Construction and Building Automation: The model can be used in building construction sites or heavy machinery setups where diverse types of nuts and parts are used, aiding workers in quickly identifying the correct components.

  4. Repair and Maintenance: The model could be useful in auto repair shops or electronics repair services to control inventories of spare parts and also to help technicians identify the exact part they need.

  5. Robotics and Automation: Robots involved in assembly lines or repairs could utilize this model to quickly identify parts, enhancing their functionality, speed, and usefulness.

Cite This Project

LicenseCC BY 4.0

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

@misc{ yolov8-u7rpc_dataset,
  title = { yolov8 Dataset },
  type = { Open Source Dataset },
  author = { PIT },
  howpublished = { \url{ https://universe.roboflow.com/pit/yolov8-u7rpc } },
  url = { https://universe.roboflow.com/pit/yolov8-u7rpc },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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