This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and semantic segmentation.
Installation
- Compile the quadtree attention operation
cd QuadTreeAttention&&python setup.py install - Install the package for each task according to each README.md in the separate directory.
Model Zoo and Baselines
We provide baselines results and model zoo in the following.
Feature matching
News! QuadTree Attention achieves the best single model performance among all public available pretrained models in image matching chanllenge 2022. Please refer to this [post].
- Quadtree on Feature matching
| Method | AUC@5 | AUC@10 | AUC@20 | Model |
|---|---|---|---|---|
| ScanNet | 24.9 | 44.7 | 61.8 | [Google]/[GitHub] |
| Megadepth | 53.5 | 70.2 | 82.2 | [Google]/[GitHub] |
Image classification
- Quadtree on ImageNet-1K
| Method | Flops | Acc@1 | Model |
|---|---|---|---|
| Quadtree-B-b0 | 0.6 | 72.0 | [Google]/[GitHub] |
| Quadtree-B-b1 | 2.3 | 80.0 | [Google]/[GitHub] |
| Quadtree-B-b2 | 4.5 | 82.7 | [Google]/[GitHub] |
| Quadtree-B-b3 | 7.8 | 83.8 | [Google]/[GitHub] |
| Quadtree-B-b4 | 11.5 | 84.0 | [Google]/[GitHub] |
Object detection and instance segmentation
- Quadtree on COCO
Baseline Detectors
| Method | Backbone | Pretrain | Lr schd | Aug | Box AP | Mask AP | Model |
|---|---|---|---|---|---|---|---|
| RetinaNet | Quadtree-B-b0 | ImageNet-1K | 1x | No | 38.4 | - | [Google]/[GitHub] |
| RetinaNet | Quadtree-B-b1 | ImageNet-1K | 1x | No | 42.6 | - | [Google]/[GitHub] |
| RetinaNet | Quadtree-B-b2 | ImageNet-1K | 1x | No | 46.2 | - | [Google]/[GitHub] |
| RetinaNet | Quadtree-B-b3 | ImageNet-1K | 1x | No | 47.3 | - | [Google]/[GitHub] |
| RetinaNet | Quadtree-B-b4 | ImageNet-1K | 1x | No | 47.9 | - | [Google]/[GitHub] |
| Mask R-CNN | Quadtree-B-b0 | ImageNet-1K | 1x | No | 38.8 | 36.5 | [Google]/[GitHub] |
| Mask R-CNN | Quadtree-B-b1 | ImageNet-1K | 1x | No | 43.5 | 40.1 | [Google]/[GitHub] |
| Mask R-CNN | Quadtree-B-b2 | ImageNet-1K | 1x | No | 46.7 | 42.4 | [Google]/[GitHub] |
| Mask R-CNN | Quadtree-B-b3 | ImageNet-1K | 1x | No | 48.3 | 43.3 | [Google]/[GitHub] |
| Mask R-CNN | Quadtree-B-b4 | ImageNet-1K | 1x | No | 48.6 | 43.6 | [Google]/[GitHub] |
Semantic Segmentation
- Quadtree on ADE20K
| Method | Backbone | Pretrain | Iters | mIoU | Model |
|---|---|---|---|---|---|
| Semantic FPN | Quadtree-b0 | ImageNet-1K | 160K | 39.9 | [Google]/[GitHub] |
| Semantic FPN | Quadtree-b1 | ImageNet-1K | 160K | 44.7 | [Google]/[GitHub] |
| Semantic FPN | Quadtree-b2 | ImageNet-1K | 160K | 48.7 | [Google]/[GitHub] |
| Semantic FPN | Quadtree-b3 | ImageNet-1K | 160K | 50.0 | [Google]/[GitHub] |
| Semantic FPN | Quadtree-b4 | ImageNet-1K | 160K | 50.6 | [Google]/[GitHub] |
Citation
@article{tang2022quadtree,
title={QuadTree Attention for Vision Transformers},
author={Tang, Shitao and Zhang, Jiahui and Zhu, Siyu and Tan, Ping},
journal={ICLR},
year={2022}
}
License
The MIT License (MIT)
Copyright (c) 2022 Shitao Tang
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.