Tube Detection Computer Vision Dataset
About Tube Detection Dataset
This Dataset is gathered for a Pick-and-Place task using the Delta Parallel Robot. The classes included in the annotation are as the following:
- Tube - Open
- Tube - Close
- Tube - Full
- Tube Rack
- Tube Available Space
- Petridish
Use Free Empty, Large_tube_closed and Large_tube_open 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": "empty, large_tube_closed, large_tube_open, medium_tube_closed, medium_tube_open"
},
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
Roboflow Agent
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ tube-detection-x52mi_dataset,
title = { Tube Detection Dataset },
type = { Open Source Dataset },
author = { Delta Parallel Robot Obstacle Avoidance Project },
howpublished = { \url{ https://universe.roboflow.com/delta-parallel-robot-obstacle-avoidance-project/tube-detection-x52mi } },
url = { https://universe.roboflow.com/delta-parallel-robot-obstacle-avoidance-project/tube-detection-x52mi },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { nov },
note = { visited on 2026-07-29 },
}