Papers › Conviformers: Convolutionally guided Vision Transformer
Conviformers: Convolutionally guided Vision Transformer
Mohit Vaishnav, Thomas Fel, Ivań Felipe Rodríguez, Thomas Serre
Vision transformers are nowadays the de-facto choice for image classification tasks. There are two broad categories of classification tasks, fine-grained and coarse-grained. In fine-grained classification, the necessity is to discover subtle differences due to the high level of similarity between sub-classes. Such distinctions are often lost as we downscale the image to save the memory and computational cost associated with vision transformers (ViT). In this work, we present an in-depth analysis and describe the critical components for developing a system for the fine-grained categorization of plants from herbarium sheets. Our extensive experimental analysis indicated the need for a better augmentation technique and the ability of modern-day neural networks to handle higher dimensional images. We also introduce a convolutional transformer architecture called Conviformer which, unlike the popular Vision Transformer (ConViT), can handle higher resolution images without exploding memory and computational cost. We also introduce a novel, improved pre-processing technique called PreSizer to resize images better while preserving their original aspect ratios, which proved essential for classifying natural plants. With our simple yet effective approach, we achieved SoTA on Herbarium 202x and iNaturalist 2019 dataset.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | Herbarium 2021 Half–Earth | Conviformer-B | Test F1 score | .719 | #1 of 1 | Archive leaderboard | report |
| Fine-Grained Image Classification | Herbarium 2022 | Conviformer-B | Test F1 score (private) | .868 | #1 of 1 | Archive leaderboard | report |
| Image Classification | iNaturalist 2019 | Conviformer-B | Top-1 Accuracy | 82.85 | #8 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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