Universe

Fruit detection Computer Vision Dataset

byUITTask:
Object Detection
License:CC BY 4.0186 views8 downloads
See all 6.8k imagesFruit detection fruit sample showing bananaFruit detection fruit sampleFruit detection fruit sample showing persimmonFruit detection fruit sample showing papayaFruit detection fruit sample showing figFruit detection fruit sample showing carambolaFruit detection fruit sampleFruit detection fruit sample showing bananaFruit detection fruit sample showing dragonfruitFruit detection fruit sample showing persimmonFruit detection fruit sample showing carambolaFruit detection fruit sample

About Fruit detection Dataset

Here are a few use cases for this project:

  1. Grocery Store Automations: This model could be used in self-checkout systems at grocery stores to help identify and price different types of fruits automatically, thus speeding up the checkout process and reducing the need for manual input.

  2. Agricultural Grading and Sorting: The model could be applied in agriculture for grading and sorting fruits based on their type during the harvesting process. This could help increase efficiency and reduce labor costs in farming operations.

  3. Health and Fitness Apps: As part of a diet and nutrition tracking app, the model could help users identify and log different types of fruits from images, aiding in accurate meal tracking and promoting a healthier lifestyle.

  4. Smart Kitchen Applications: Integrated into a smart fridge application, the model could identify the types of fruits stored in the fridge, making it easier for the user to track what they have and when they might need to restock.

  5. Education: The model could be used in educational apps or games that aim to teach children (or anyone learning about fruits) to recognize and identify different types of fruits.

Use Free Apple, Banana and Tomato 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": "apple, banana, tomato, lemon, grape"
  },
  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 

Cite This Project

LicenseCC BY 4.0

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

@misc{ fruit-detection-vt6xn_dataset,
  title = { Fruit detection Dataset },
  type = { Open Source Dataset },
  author = { UIT },
  howpublished = { \url{ https://universe.roboflow.com/uit-3a7w9/fruit-detection-vt6xn } },
  url = { https://universe.roboflow.com/uit-3a7w9/fruit-detection-vt6xn },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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