---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': no_tomato
          '1': tomato
  splits:
  - name: original
    num_bytes: 8384551.0
    num_examples: 49
  - name: augmented
    num_bytes: 36005046.0
    num_examples: 490
  download_size: 44391487
  dataset_size: 44389597.0
configs:
- config_name: default
  data_files:
  - split: original
    path: data/original-*
  - split: augmented
    path: data/augmented-*
---


## Dataset Summary
This dataset contains **real-world photographs** labeled for the presence of tomatoes.  
It is designed for **binary image classification** tasks, where the model predicts whether an image contains a tomato (`1`) or not (`0`).

- **Original size:** 49 images  
- **Augmented size:** 490 images  
- **Task type:** Image Classification (binary)  
- **Goal:** Train models to distinguish between images with and without tomatoes  


## Data Splits
- No predefined train/test split.  
- Users can apply their own strategy (e.g., 80/20 split or k-fold cross-validation).
- 

## Intended Uses
- **Binary Classification:** Distinguish between images containing tomatoes vs. not.  
- **Computer Vision Training:** Baseline dataset for testing CNNs or transfer learning models.  
- **Educational Use:** Demonstrates dataset augmentation in image classification (49 → 490 samples).  


## Labels
- `0` → Image **does not** contain tomatoes  
- `1` → Image **contains** tomatoes