item-detector Computer Vision Model

bylabelckaiTask:
Object Detection
License:MIT

How to use the item-detector Detection API

Try This Model

Drop an image here or click to upload

Or try a test image 

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="item-detector-dbggt/1")
Give your agent everything it needs

Or, Use Free 1_box_plastic, 2_board_white and 3_board_black Detection API

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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": "1_box_plastic, 2_board_white, 3_board_black, 4_cable_black, 5_cable_white"
  },
  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 

About item-detector Model

A description for this project has not been published yet.

Cite This Project

LicenseMIT

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

@misc{ item-detector-dbggt_dataset,
  title = { item-detector Dataset },
  type = { Open Source Dataset },
  author = { labelckai },
  howpublished = { \url{ https://universe.roboflow.com/labelckai/item-detector-dbggt } },
  url = { https://universe.roboflow.com/labelckai/item-detector-dbggt },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2026 },
  month = { jan },
  note = { visited on 2026-07-29 },
}

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