Official PyTorch implementation of "EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm" that improves our previous work "Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model, NeurIPS'21", Code.

Abstract Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation. Then inspired by effective EA variants, we propose a novel pyramid EATFormer backbone that only contains the proposed EA-based Transformer (EAT) block, which consists of three residual parts, \ie, Multi-Scale Region Aggregation (MSRA), Global and Local Interaction (GLI), and Feed-Forward Network (FFN) modules, to model multi-scale, interactive, and individual information separately. Moreover, we design a Task-Related Head (TRH) docked with transformer backbone to complete final information fusion more flexibly and improve a Modulated Deformable MSA (MD-MSA) to dynamically model irregular locations. Massive quantitative and quantitative experiments on image classification, downstream tasks, and explanatory experiments demonstrate the effectiveness and superiority of our approach over State-Of-The-Art (SOTA) methods. \Eg, our Mobile (1.8M), Tiny (6.1M), Small (24.3M), and Base (49.0M) models achieve 69.4, 78.4, 83.1, and 83.9 Top-1 only trained on ImageNet-1K with naive training recipe; EATFormer-Tiny/Small/Base armed Mask-R-CNN obtain 45.4/47.4/49.0 box AP and 41.4/42.9/44.2 mask AP on COCO detection, surpassing contemporary MPViT-T, Swin-T, and Swin-S by 0.6/1.4/0.5 box AP and 0.4/1.3/0.9 mask AP separately with less FLOPs; Our EATFormer-Small/Base achieve 47.3/49.3 mIoU on ADE20K by Upernet that exceeds Swin-T/S by 2.8/1.7.

EATFormer models and train/test codes will be released soon ...