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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': no_tomato |
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'1': tomato |
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splits: |
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- name: original |
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num_bytes: 8384551.0 |
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num_examples: 49 |
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- name: augmented |
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num_bytes: 36005046.0 |
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num_examples: 490 |
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download_size: 44391487 |
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dataset_size: 44389597.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: original |
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path: data/original-* |
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- split: augmented |
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path: data/augmented-* |
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--- |
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## Dataset Summary |
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This dataset contains **real-world photographs** labeled for the presence of tomatoes. |
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It is designed for **binary image classification** tasks, where the model predicts whether an image contains a tomato (`1`) or not (`0`). |
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- **Original size:** 49 images |
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- **Augmented size:** 490 images |
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- **Task type:** Image Classification (binary) |
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- **Goal:** Train models to distinguish between images with and without tomatoes |
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## Data Splits |
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- No predefined train/test split. |
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- Users can apply their own strategy (e.g., 80/20 split or k-fold cross-validation). |
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- |
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## Intended Uses |
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- **Binary Classification:** Distinguish between images containing tomatoes vs. not. |
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- **Computer Vision Training:** Baseline dataset for testing CNNs or transfer learning models. |
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- **Educational Use:** Demonstrates dataset augmentation in image classification (49 → 490 samples). |
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## Labels |
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- `0` → Image **does not** contain tomatoes |
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- `1` → Image **contains** tomatoes |
