Iris314/Food_tomatoes_dataset

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