minerales en ceniza Computer Vision Model
How to use the minerales en ceniza Detection API
Try This Model
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Model type: YOLO-NAS Object Detection (Accurate)
Dataset: minerales-en-ceniza/3 (105 images)
Checkpoint: minerales-en-ceniza/1
Dec 3, 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="minerales-en-ceniza/3")Give your agent everything it needs
Or, Use Free Accidentales, Anfibol and Conglomerado vidrio 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": "accidentales, anfibol, conglomerado vidrio, cristobalita, feldespato"
},
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 minerales en ceniza Model
A description for this project has not been published yet.
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Cite This Project
LicenseMITIf you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ minerales-en-ceniza_dataset,
title = { minerales en ceniza Dataset },
type = { Open Source Dataset },
author = { Fotos de ceniza },
howpublished = { \url{ https://universe.roboflow.com/fotos-de-ceniza-ndhul/minerales-en-ceniza } },
url = { https://universe.roboflow.com/fotos-de-ceniza-ndhul/minerales-en-ceniza },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { dec },
note = { visited on 2026-07-29 },
}