A Comprehensive Study on Deep Image Classification with Small Datasets

Chandrarathne, Gayani; Thanikasalam, Kokul; Pinidiyaarachchi, Amalka

doi:10.1007/978-981-15-1289-6_9

Cited by 7 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A larger dataset allows for more epochs and a potentially improved validation or testing accuracy, as the model can learn from a more extensive variety of objects [36]. Nevertheless, studies confirm that the training and classification work well even when using small datasets [59,60]. Mentioning the dataset size, it is necessary to mention that in this study, no test set was created, as it would be too small and imply statistical uncertainty, but it should be considered in future works, allowing for independent testing of trained classifiers.…”

Section: Discussionmentioning

confidence: 99%

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

Hrdina,

Surový

2023

Remote Sensing

View full text Add to dashboard Cite

The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models.

show abstract

Section: Discussionmentioning

confidence: 99%

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

Hrdina,

Surový

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The effectiveness of tuning the number of convolutional layers to classify small datasets is proven in Chandrarathne et al ( 2020 ). In addition, related experiments suggest that by employing a very low learning rate (LR), the accuracy of classification of small datasets can be greatly increased.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

Zhao

Rahaman

et al. 2022

Front. Microbiol.

View full text Add to dashboard Cite

In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyper-parameters tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet, and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the vision transformer (ViT) model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

show abstract

“…The effectiveness of adjusting the number of convolutional layers to classify small data sets is proven in [33]. In addition, related experiments suggest that by employing a very low learning rate, the accuracy of classification of small data sets can be greatly increased.…”

Section: The Impact Of Small Data Sets On Image Classificationmentioning

confidence: 99%

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

Zhao¹,

Li²,

Rahaman³

et al. 2021

Preprint

View full text Add to dashboard Cite

In recent years, deep learning has made brilliant achievements in image classification. However, image classification of small datasets is still not obtained good research results. This article first briefly explains the application and characteristics of convolutional neural networks and visual transformers. Meanwhile, the influence of small data set on classification and the solution are introduced. Then a series of experiments are carried out on the small datasets by using various models, and the problems of some models in the experiments are discussed. Through the comparison of experimental results, the recommended deep learning model is given according to the model application environment. Finally, we give directions for future work.

show abstract

A Comprehensive Study on Deep Image Classification with Small Datasets

Cited by 7 publications

References 26 publications

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

Contact Info

Product

Resources

About