orange-ripe Computer Vision Model
How to use the orange-ripe Detection API
Try This Model
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Model type: YOLOv11 Object Detection (Fast)
Dataset: orange-ripe/2 (7468 images)
Checkpoint: COCOn
Mar 6, 2026
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="orange-ripe/2")Give your agent everything it needs
Or, Use Free Orange 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": "orange"
},
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 orange-ripe Model
orange detect fruit for mohmmed samara and it will be
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{ orange-ripe_dataset,
title = { orange-ripe Dataset },
type = { Open Source Dataset },
author = { Agris Workspace },
howpublished = { \url{ https://universe.roboflow.com/agris-workspace/orange-ripe } },
url = { https://universe.roboflow.com/agris-workspace/orange-ripe },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2026 },
month = { mar },
note = { visited on 2026-07-29 },
}