find-snacks Computer Vision Dataset
How to use the find-snacks Detection API
Try This Model
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Model type: Roboflow Instant
Dataset: find-snacks/1 (30 images)
Model ID: computer-vision-ti2a-2025-s8pkb/find-snacks-instant-1
Dec 10, 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="computer-vision-ti2a-2025-s8pkb/find-snacks-instant-1")Give your agent everything it needs
Or, Use Free Beng beng, Chocolatos and Nabati wafer 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": "beng beng, chocolatos, nabati wafer, oreo, super star"
},
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 find-snacks Model
model for detection snacks like beng beng, superstar, chocolatos, nabati wafer, and oreo mini
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{ find-snacks_dataset,
title = { find-snacks Dataset },
type = { Open Source Dataset },
author = { Computer Vision TI2A 2025 },
howpublished = { \url{ https://universe.roboflow.com/computer-vision-ti2a-2025-s8pkb/find-snacks } },
url = { https://universe.roboflow.com/computer-vision-ti2a-2025-s8pkb/find-snacks },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2026 },
month = { jun },
note = { visited on 2026-07-29 },
}










