mug Computer Vision Model
How to use the mug Detection API
Try This Model
Drop an image here or click to upload
Or try a test image
Model type: Roboflow 3.0 Object Detection (Fast)
Dataset: mug-yjwgn/2 (100 images)
Checkpoint: COCOn
May 13, 2024
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="mug-yjwgn/2")Give your agent everything it needs
Or, Use Free Mug with handle on leftside and Mug with handle on right side 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": "mug with handle on leftside, mug with handle on right side"
},
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 mug Model
A description for this project has not been published yet.
Roboflow Agent
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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{ mug-yjwgn_dataset,
title = { mug Dataset },
type = { Open Source Dataset },
author = { mug },
howpublished = { \url{ https://universe.roboflow.com/mug-sdhau/mug-yjwgn } },
url = { https://universe.roboflow.com/mug-sdhau/mug-yjwgn },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { may },
note = { visited on 2026-07-29 },
}