Fruits and Vegetables Image Recognition Dataset
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### About the Dataset #### Context This dataset encompasses images of various fruits and vegetables, providing a diverse collection for image recognition tasks. The included food items are: - **Fruits**: Banana, Apple, Pear, Grapes, Orange, Kiwi, Watermelon, Pomegranate, Pineapple, Mango - **Vegetables**: Cucumber, Carrot, Capsicum, Onion, Potato, Lemon, Tomato, Radish, Beetroot, Cabbage, Lettuce, Spinach, Soybean, Cauliflower, Bell Pepper, Chilli Pepper, Turnip, Corn, Sweetcorn, Sweet Potato, Paprika, Jalapeño, Ginger, Garlic, Peas, Eggplant #### Content The dataset is organized into three main folders: - **Train**: Contains 100 images per category. - **Test**: Contains 10 images per category. - **Validation**: Contains 10 images per category. Each of these folders is subdivided into specific folders for each type of fruit and vegetable, containing respective images. #### Data Collection The images in this dataset were sourced using Bing Image Search for a personal project focused on image recognition of food items. The creator does not hold the rights to any of the images included in this dataset. If you are the owner of any image and have concerns regarding its use, please contact the creator to request its removal. The creator will promptly comply with any such requests to ensure all legal obligations are met. **Disclaimer**: Users of this dataset are responsible for ensuring that their use of the images complies with applicable copyright laws and regulations. The creator assumes no responsibility for any legal issues that may arise from the use of this dataset. It is recommended to use the dataset for educational and non-commercial purposes only and to seek legal counsel if you have specific concerns about copyright compliance. #### Inspiration The primary motivation behind creating this dataset was to develop an application capable of recognizing food items from photographs. The application aims to suggest various recipes that can be prepared using the identified ingredients. #### Citation ``` Kritik Seth, "Fruits and Vegetables Image Recognition Dataset," Kaggle 2020 [https://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition] ```
{'Context': '本数据集涵盖各式水果与蔬菜的图像，构成了一个多元化的图像识别任务数据集。其中包含的食品项目包括： - **水果**：香蕉、苹果、梨、葡萄、橙子、猕猴桃、西瓜、石榴、菠萝、芒果 - **蔬菜**：黄瓜、胡萝卜、甜椒、洋葱、土豆、柠檬、番茄、萝卜、甜菜根、卷心菜、生菜、菠菜、大豆、花菜、青椒、辣椒、萝卜、玉米、甜玉米、红薯、辣椒粉、墨西哥辣椒、姜、蒜、豌豆、茄子 #### 内容 本数据集分为三个主要文件夹： - **训练集**：每个类别包含100张图像。 - **测试集**：每个类别包含10张图像。 - **验证集**：每个类别包含10张图像。 每个文件夹均细分为针对每种水果和蔬菜的特定文件夹，包含相应的图像。 #### 数据收集 本数据集中的图像系通过Bing图像搜索获取，用于一个专注于食品识别的个人项目。数据集的创建者不拥有数据集中任何图像的版权。如果您是任何图像的所有者，并对其使用有疑问，请与创建者联系，请求删除。创建者将迅速响应此类请求，以确保满足所有法律义务。 **免责声明**：本数据集的使用者有责任确保其使用图像符合适用的版权法律和法规。创建者不对由此产生的任何法律问题承担责任。建议仅将数据集用于教育和非商业目的，并在对版权合规性有具体疑问时寻求法律咨询。 #### 灵感 创建本数据集的主要动机是开发一个能够从照片中识别食品的应用程序。该应用程序旨在根据识别出的食材提出各种食谱建议。 #### 引用 Kritik Seth, 《水果与蔬菜图像识别数据集》, Kaggle 2020 [https://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition]'}
