GroundingDINO Computer Vision Dataset
How to use the GroundingDINO Detection API
Try This Model
Drop an image here or click to upload
Or try a test image
Model type: Roboflow Instant
Dataset: groundingdino-gw4uz/1 (200 images)
Model ID: ucd/groundingdino-gw4uz-instant-1
Sep 4, 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="ucd/groundingdino-gw4uz-instant-1")Give your agent everything it needs
Or, Use Free Vehicle, Door and Window 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": "vehicle, door, window, building, light"
},
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 GroundingDINO Model
Belfast City Google Street View Images Object Detection
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{ groundingdino-gw4uz_dataset,
title = { GroundingDINO Dataset },
type = { Open Source Dataset },
author = { UCD },
howpublished = { \url{ https://universe.roboflow.com/ucd/groundingdino-gw4uz } },
url = { https://universe.roboflow.com/ucd/groundingdino-gw4uz },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { sep },
note = { visited on 2026-07-29 },
}










