# Fine-Tuning Models Comparisons on Garbage Classification for Recyclability

arXiv: https://arxiv.org/abs/1908.04393 · HTML: https://mlsanity.com/paper/1908.04393/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Towards artificially intelligent recycling Improving image processing for waste classification](https://mlsanity.com/paper/2108.06274/) (2021)
- [Medical Waste Sorting: a computer vision approach for assisted primary sorting](https://mlsanity.com/paper/2303.04720/) (2023)
- [An improved EfficientNetV2 for garbage classification](https://mlsanity.com/paper/2503.21208/) (2025)
- [Real-Time Trash Detection for Modern Societies using CCTV to Identifying Trash by utilizing Deep Convolutional Neural Network](https://mlsanity.com/paper/2109.09611/) (2021)
- [A Smart Recycling Bin Using Waste Image Classification At The Edge](https://mlsanity.com/paper/2210.00448/) (2022)
- [Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)](https://mlsanity.com/paper/2503.02241/) (2025)

## Abstract (arXiv)

In this study, it is aimed to develop a deep learning application which detects types of garbage into trash in order to provide recyclability with vision system. Training and testing will be performed with image data consisting of several classes on different garbage types. The data set used during training and testing will be generated from original frames taken from garbage images. The data set used for deep learning structures has a total of 2527 images with 6 different classes. Half of these images in the data set were used for training process and remaining part were used for testing procedure. Also, transfer learning was used to obtain shorter training and test procedures with and higher accuracy. As fine-tuned models, Alexnet, VGG16, Googlenet and Resnet structures were carried. In order to test performance of classifiers, two different classifiers are used as Softmax and Support Vector Machines. 6 different type of trash images were correctly classified the highest accuracy with GoogleNet+SVM as 97.86%.