Rose Image Classification Dataset

Description

The Rose Image Classification Dataset comprises a total of 3,000 images, categorized into four distinct classes based on rose color: Red, Pink, Yellow, and White. The dataset was developed to support deep learning-based image classification and agricultural research applications. 📊 Class-wise Distribution Red: 1,200 images Pink: 900 images Yellow: 500 images White: 400 images The dataset exhibits a moderate class imbalance, with the Red class containing the highest number of samples and the White class the lowest. This distribution reflects real-world data collection scenarios and enables evaluation of model performance under imbalanced conditions.

Steps to reproduce

Download the dataset from Mendeley Data and extract the compressed file. Organize the dataset into the provided directory structure (train, val, test). Install the required Python libraries such as TensorFlow or PyTorch, along with supporting packages (e.g., NumPy, OpenCV). Preprocess the images (e.g., resizing, normalization) before feeding them into the model. Train a deep learning model (e.g., CNN) using the training set. Validate the model performance using the validation set during training. Evaluate the final model performance using the test set.

Institutions

• Daffodil International University

  Dhaka District, Dhaka

Categories

Licence