Pommes Pont-Neuf
Description
Dataset Description: Good and Bad French Fries Classification 1. Overview This dataset comprises over 500 samples of French fries (pommes pont-neuf) categorized as good or bad based on their visual and qualitative attributes. The dataset is designed for machine learning applications, particularly for training and evaluating models in image-based food quality assessment. 2. Data Collection Device Used: Redmi 10 Prime mobile camera Lighting Condition: Natural daylight Background: White Number of Samples: More than 500 Categories: Good French Fries: Fries with uniform shape, golden-brown color, crisp texture, and no visible burns or defects. Bad French Fries: Fries that are overcooked (burnt edges), undercooked (pale appearance), irregularly shaped, broken, or with visible black spots. 3. Image Specifications Resolution: Based on the Redmi 10 Prime's camera specifications, the images are expected to have high clarity, making it easier to distinguish quality differences. Format: Likely captured in JPEG or PNG format. Framing: Single fry or multiple fries per image, uniformly arranged against a white background. 4. Applications Automated food quality classification Training deep learning models for food inspection Enhancing fast-food industry quality control Computer vision applications in food safety This dataset provides a solid foundation for AI-driven quality assessment of French fries, ensuring consistency and efficiency in food evaluation.