dadi-poti Computer Vision Model
How to use the dadi-poti Segmentation API
Try This Model
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Model type: Roboflow 3.0 Instance Segmentation (Fast)
Dataset: dadi-poti-omtgz/3 (4291 images)
Checkpoint: COCOn-seg
Feb 15, 2025
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="dadi-poti-omtgz/3")Give your agent everything it needs
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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": "crack, brick, discolouration, spalling"
},
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 dadi-poti Model
A description for this project has not been published yet.
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Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ dadi-poti-omtgz_dataset,
title = { dadi-poti Dataset },
type = { Open Source Dataset },
author = { mujamil },
howpublished = { \url{ https://universe.roboflow.com/mujamil/dadi-poti-omtgz } },
url = { https://universe.roboflow.com/mujamil/dadi-poti-omtgz },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { feb },
note = { visited on 2026-07-29 },
}