NexTech14_Waste_Dataset: A Benchmark Image Collection for Intelligent Waste Classification and Recycling Systems

It comprises over 8,996 RGB images. Each image has been manually verified, preprocessed, and standardized to 224×224 pixels, include extensions (.jpg, .png) format and ensuring balanced representation and visual consistency. The dataset captures real-world variations in lighting, texture, and background to enhance robustness in model training and evaluation.

After augmentation, the dataset was partitioned using a 70:15:15 ratio for training(train), validation(val), and testing(test). The splitfolders library was used with a fixed random seed (seed = 42) to ensure reproducibility and unbiased evaluation. Optimized for Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid deep learning frameworks, this dataset enables reproducible experimentation for automated waste recognition, robotics-based sorting, and environmental monitoring.

Structured with clear folder hierarchies and annotated metadata, NexTech14_Waste_Dataset serves as a reliable benchmark for academic research and industrial innovation. Released under the CC BY-NC 4.0, it encourages open collaboration toward building intelligent, eco-sustainable systems for a cleaner future.