AOP-YOLO Computer Vision Model
How to use the AOP-YOLO Detection API
Try This Model
Drop an image here or click to upload
Or try a test image
Model type: YOLOv11 Object Detection (Fast)
Dataset: aop-yolo/3 (6721 images)
Checkpoint: top-yyyef/2
May 23, 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="aop-yolo/3")Give your agent everything it needs
Or, Use Free Fall-Detected and NotFall 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": "Fall-Detected, NotFall"
},
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 AOP-YOLO Model
A description for this project has not been published yet.
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{ aop-yolo_dataset,
title = { AOP-YOLO Dataset },
type = { Open Source Dataset },
author = { PROJ3 },
howpublished = { \url{ https://universe.roboflow.com/proj3/aop-yolo } },
url = { https://universe.roboflow.com/proj3/aop-yolo },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { may },
note = { visited on 2026-07-29 },
}










