Aspergillus-YOLO Computer Vision Model
How to use the Aspergillus-YOLO Detection API
Try This Model
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Model type: Roboflow RF-DETR Object Detection (Nano)
Dataset: aspergillus-yolo/2 (32 images)
Mar 27, 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="aspergillus-yolo/2")Give your agent everything it needs
Or, Use Free Aspergillus and Single-Label 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": "aspergillus, Single-Label"
},
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 Aspergillus-YOLO Model
This process involves the localization and classification of Aspergillus species using bounding box annotations in the YOLO framework
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{ aspergillus-yolo_dataset,
title = { Aspergillus-YOLO Dataset },
type = { Open Source Dataset },
author = { Hiranurs Workspace },
howpublished = { \url{ https://universe.roboflow.com/hiranurs-workspace/aspergillus-yolo } },
url = { https://universe.roboflow.com/hiranurs-workspace/aspergillus-yolo },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2026 },
month = { mar },
note = { visited on 2026-07-29 },
}