Abstract
Millions of people throughout the world have been harmed by plastic pollution. There are microscopic pieces of plastic in the food we eat, the water we drink, and even the air we breathe. Every year, the average human consumes 74,000 microplastics, which has a significant impact on their health. This pollution must be addressed before it has a significant negative influence on the population. This research benchmarks six state-of-the-art convolutional neural network models pre-trained on the ImageNet Dataset. The models Resnet-50, ResNeXt, MobileNet_v2, DenseNet, SchuffleNet and AlexNet were tested and evaluated on the WaDaBa plastic dataset, to classify plastic types based on their resin codes by integrating the power of transfer learning. The accuracy and training time for each model has been compared in this research. Due to the imbalance in the data, the under-sampling approach has been used. The ResNeXt model attains the highest accuracy in fourteen minutes.
Highlights
Plastic finds itself in everyday human activities
This research contributes towards benchmarking of pre-trained models and concluding that the ResNeXt model achieves the highest accuracy on the WaDaBa dataset from the list of pre-trained models specified in this paper
The metrics used to benchmark the models on the WaDaBa dataset are accuracy and loss
Summary
Plastic finds itself in everyday human activities. The mass production of plastic was introduced in 1907 by Leo Baekeland, proved to be a boon to humankind[1]. Plastic has increasingly become an everyday necessity for humanity. The population explosion has a critical part in increasing domestic plastic usage[2]. Lightweight plastics have a crucial role in the transportation industry.
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