Automatic garbage classification using deep learning is a technology that uses machine learning algorithms to categorize different types of waste. This technology aims to automate the process of waste management by using image recognition techniques to classify garbage. The system is designed to identify and sort different waste materials, including plastic, paper, metal, glass, and organic waste. Deep learning algorithms, such as convolutional neural networks, are used to train the system on large datasets of waste images. The system is capable of detecting and classifying waste items in real-time, making it a useful tool for waste management organizations and municipalities. The results of this technology can help to reduce waste pollution, improve recycling rates, and increase environmental sustainability. Automatic garbage classification using the 201 algorithm in deep learning is a technology that utilizes the ResNet-201 architecture to classify various types of waste. The ResNet-201 architecture is a deep convolutional neural network that has shown significant performance in image classification tasks. This technology aims to automate the process of waste management by using image recognition techniques to classify garbage. The system is designed to identify and sort different waste materials, including plastic, paper, metal, glass, and organic waste. The ResNet-201 algorithm is trained on large datasets of waste images to recognize and classify waste items accurately. The system can detect and classify waste items in real-time, making it a useful tool for waste management organizations and municipalities. The results of this technology can help to reduce waste pollution, improve recycling rates, and increase environmental sustainability.