법륜D스님의 의지 Computer Vision Model
How to use the 법륜D스님의 의지 Segmentation API
Try This Model
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Model type: Roboflow 3.0 Instance Segmentation (Fast)
Dataset: d-smf1n/1 (1017 images)
Checkpoint: COCO
Nov 27, 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="d-smf1n/1")Give your agent everything it needs
Or, Use Free Fire and Smoke 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": "Fire, Smoke"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
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Detecting classes:
Or try a test image
About 법륜D스님의 의지 Model
fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke fire and smoke
Roboflow Agent
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Cite This Project
LicensePublic DomainIf you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ d-smf1n_dataset,
title = { 법륜D스님의 의지 Dataset },
type = { Open Source Dataset },
author = { new-workspace-nmits },
howpublished = { \url{ https://universe.roboflow.com/new-workspace-nmits/d-smf1n } },
url = { https://universe.roboflow.com/new-workspace-nmits/d-smf1n },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2026 },
month = { jan },
note = { visited on 2026-07-29 },
}