Workshop Camera Traps, AI and Ecology Compare transformers and convolutional models Comparison between transformers and convolutional models for fine-grained classification of insects
This paper explores how different AI models can help identify insect species, which is important for understanding biodiversity. It compares traditional convolutional models with newer transformer models to see which performs better.
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- 1 The ability to identify the insects that inhabit the ecosystems is one of the main steps to understanding them.
- 2 The conventional identification technique is to cross-validate the image with the regional field guides, online sources, and field experts.
- 3 We evaluate the models on iNaturalist and Artportalen limited to Odonata and Coleoptera from Europe, which are collected from different communities of citizen science than training.
- 4 In the domain of Insecta extensive work has been done to identify different species in different orders e.g. Lepidoptera, Coleoptera, Odonata, Orthoptera, and Hymenoptera.
Introduction
In the biodiversity monitoring field, we talk about species as classes and order as a superclass. The species are usually defined by domain experts (taxonomists) based on morphology or molecular data.
In this paper, we focus on two orders of Insecta: Coleoptera and Odonata in Europe.
In the order Coleoptera, there are four main suborders: Archostemata, Myxophaga, Adephaga, and Polyphaga, and more than 130,000 species in Europe .
To answer this question, we first consider the limitation of taxonomical datasets considered for this study.
Methodology
Fine-grained classification task aims to differentiate between classes that belong to the same superclass. Despite its significance, the fine-grained task in biodiversity has posed two key challenges: 1) The inter-class variances are often extremely subtle, thus requiring highly discriminative representation for effective classification; 2) As the rarity of the species increases, there are fewer training samples per category, impeding the performance of large-data favoured methods.
Study Design
The results are presented to address the fine-grained task at the species and the morph\/sex levels.
Moreover, we do not observe equal interest in the application of transformer-based models in this task.
Results & Findings
In Fig. 1 , we present some samples from the datasets of interest in this paper. On the second row, we present samples from the order Odonata, as we note the subjects in the images all show a similar shape and dimension and sometimes similar colour, but they all belong to different species.
- In Fig. 1 , we present some samples from the datasets of interest in this paper.
- On the second row, we present samples from the order Odonata, as we note the subjects in the images all show a similar shape and dimension.
- The ability to identify the insects that inhabit the ecosystems is one of the main steps to understanding them.
- The conventional identification technique is to cross-validate the image with the regional field guides, online sources, and field experts.
- Unfortunately, ViT is data-hungry and the lack of training data may impede its application in fine-grained tasks.
We evaluate the models on iNaturalist and Artportalen limited to Odonata and Coleoptera from Europe, which are collected from different communities of citizen science than training.
The ability to identify the insects that inhabit the ecosystems is one of the main steps to understanding them.
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Models
This section defines the three families of models considered for image classification: fully-convolutional (EfficientNet_v2), fully-transformer (ViT), and hybrid models. It discusses their structures, advantages, and limitations in the context of fine-grained classification.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1: Sample images from the datasets of interest, showcasing the morphological similarities among species.. Illustrates the challenges of fine-grained classification due to phenotypic similarities.
Conclusion
The species within an order are closely related and look-alike, i.e. many of them shared colours and characteristics, therefore the fine-grained tasks in this field are particularly challenging. We conclude that the ability of ViTAEv2 to learn the singularity of each species based on details of the images is stronger than fully-convolutional and fully-transformer based models.
Moreover, for the imago morph, we evaluate the impact of sex on the performance of the models and we can conclude that all three models manifest a good performance with all three sexes.
Therefore, these misclassifications make sense and open new discussions on the proper use of these methods in ecology to exploit the possibility of using such models to help taxonomist to identify difficult images at the species level.
Frequently Asked Questions
In Coleoptera, many species are small with discriminating characters often difficult to see. A further factor complicating identification is the within-species variability due to differences between life stages, sexes and regional or seasonal variation.
The results are presented to address the fine-grained task at the species and the morph\/sex levels. Furthermore, there is no experimentation on the most modern models from computer vision for this task.
The ability to identify the insects that inhabit the ecosystems is one of the main steps to understanding them. The conventional identification technique is to cross-validate the image with the regional field guides, online sources, and field experts.
The species within an order are closely related and look-alike, i.e. many of them shared colours and characteristics, therefore the fine-grained tasks in this field are particularly challenging. We conclude that the ability of ViTAEv2 to learn the singularity of each species.
We evaluate the models on iNaturalist and Artportalen limited to Odonata and Coleoptera from Europe, which are collected from different communities of citizen science than training. To answer this question, we first consider the limitation of taxonomical datasets considered for this study.
This paper explores how different AI models can help identify insect species, which is important for understanding biodiversity. It compares traditional convolutional models with newer transformer models to see which performs better.