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Tomatoe Computer Vision Dataset

byTeknikhgskolanTask:
Object Detection
License:CC BY 4.064 views2 downloads
See all 3k imagesTomatoe tomatoe sampleTomatoe tomatoe sampleTomatoe tomatoe sampleTomatoe tomatoe sampleTomatoe tomatoe sample showing gingerTomatoe tomatoe sampleTomatoe tomatoe sample showing eggTomatoe tomatoe sample showing lentilsTomatoe tomatoe sampleTomatoe tomatoe sample showing beansTomatoe tomatoe sample showing asparagusTomatoe tomatoe sample showing chicken

About Tomatoe Dataset

Here are a few use cases for this project:

  1. Recipe Analysis: The "Tomatoe" model can be used to analyze photos of food and identify recipes used based on the ingredients present. It can help users to recreate meals they've seen in pictures but lack the recipe for, by categorizing and outlining the ingredients listed.

  2. Diet and Nutritional Apps: Make tracking food intake and nutritional value easier by allowing users to simply take a picture of their meals. The model would recognize the food items and provide an estimate of the nutritional value, including calories, proteins, fats, and carbohydrates.

  3. Grocery Shopping Aid: Assist in shopping for groceries by identifying vegetables, fruits, proteins, and more in real-time or in pictures. Users can keep track of what they've already placed in their shopping cart and what is left on their list.

  4. Smart Kitchen Inventory: Scan the refrigerator or pantry and maintain a detailed inventory based on visual identification. Suggest recipes based on the food items available at home.

  5. Agriculture Applications: Help farmers or gardeners identify and categorize their crops including distinguishing between types of tomatoes, spotting disease or ripeness by training on applicable datasets.

Use Free Banana, Carrot and Broccoli 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": "banana, carrot, broccoli, chicken, lemon"
  },
  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{ tomatoe-jf9oo_dataset,
  title = { Tomatoe Dataset },
  type = { Open Source Dataset },
  author = { Teknikhgskolan },
  howpublished = { \url{ https://universe.roboflow.com/teknikhgskolan/tomatoe-jf9oo } },
  url = { https://universe.roboflow.com/teknikhgskolan/tomatoe-jf9oo },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { may },
  note = { visited on 2026-07-29 },
}

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