Universe

Snacks_detection Computer Vision Model

byPOSCOAIAcademyTask:
Object Detection
License:CC BY 4.0250 views24 downloads

How to use the Snacks_detection Detection API

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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="snacks_detection-d2edm/2")
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Or, Use Free Banana, Jelly and Kimbap 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": "banana, jelly, kimbap, ramen, snack_chicken"
  },
  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 
See all 4.8k imagesSnacks_detection snacks sample showing bananaSnacks_detection snacks sample showing jellySnacks_detection snacks sample showing ramenSnacks_detection snacks sample showing ramenSnacks_detection snacks sample showing snack_chickenSnacks_detection snacks sample showing snack_chickenSnacks_detection snacks sample showing jellySnacks_detection snacks sample showing ramenSnacks_detection snacks sample showing jellySnacks_detection snacks sample showing ramenSnacks_detection snacks sample showing ramenSnacks_detection snacks sample showing jelly

About Snacks_detection Model

Here are a few use cases for this project:

  1. Smart Grocery Stores: Implement the model in smart fridges or shelves to track inventory and automatically reorder snacks once they reach a certain threshold. It can also assist in gathering consumer behavior data by studying which snacks are picked up more frequently.

  2. Food Retail Marketing: Can be used to analyze customer preferences in supermarkets or convenience stores and optimize store layout or promotional strategies based on the most frequently picked snacks.

  3. Nutritional Analysis: Can be employed in diet and health apps. Users can simply take a photo of their snacks and the model can identify what they're eating, providing them with nutritional information instantly.

  4. Customized Vending Machines: Upgrade vending machines to include the model, helping them offer a more personalized experience by suggesting snacks based on past choices or even identifying low stock items in real time.

  5. Cooking and Recipe Apps: Integrate the model into a cooking app where users can input a photo of a snack they want to make, and the app can identify the snack and provide a related recipe.

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ snacks_detection-d2edm_dataset,
  title = { Snacks_detection Dataset },
  type = { Open Source Dataset },
  author = { POSCOAIAcademy },
  howpublished = { \url{ https://universe.roboflow.com/poscoaiacademy/snacks_detection-d2edm } },
  url = { https://universe.roboflow.com/poscoaiacademy/snacks_detection-d2edm },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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