- (2022/08/09) Application examples for polyp segmentation (polyp-pvt) and vision-language modeling.
- (2020/06/21) Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.
The image is from Transformers: Revenge of the Fallen.
This repository contains the official implementation of PVTv1 & PVTv2 in image classification, object detection, and semantic segmentation tasks.
Classification configs & weights see >>>here<<<.
- PVTv2 on ImageNet-1K
| Method | Size | Acc@1 | #Params (M) |
|---|---|---|---|
| PVTv2-B0 | 224 | 70.5 | 3.7 |
| PVTv2-B1 | 224 | 78.7 | 14.0 |
| PVTv2-B2-Linear | 224 | 82.1 | 22.6 |
| PVTv2-B2 | 224 | 82.0 | 25.4 |
| PVTv2-B3 | 224 | 83.1 | 45.2 |
| PVTv2-B4 | 224 | 83.6 | 62.6 |
| PVTv2-B5 | 224 | 83.8 | 82.0 |
- PVTv1 on ImageNet-1K
| Method | Size | Acc@1 | #Params (M) |
|---|---|---|---|
| PVT-Tiny | 224 | 75.1 | 13.2 |
| PVT-Small | 224 | 79.8 | 24.5 |
| PVT-Medium | 224 | 81.2 | 44.2 |
| PVT-Large | 224 | 81.7 | 61.4 |
Detection configs & weights see >>>here<<<.
- PVTv2 on COCO
| Method | Backbone | Pretrain | Lr schd | Aug | box AP | mask AP |
|---|---|---|---|---|---|---|
| RetinaNet | PVTv2-b0 | ImageNet-1K | 1x | No | 37.2 | - |
| RetinaNet | PVTv2-b1 | ImageNet-1K | 1x | No | 41.2 | - |
| RetinaNet | PVTv2-b2 | ImageNet-1K | 1x | No | 44.6 | - |
| RetinaNet | PVTv2-b3 | ImageNet-1K | 1x | No | 45.9 | - |
| RetinaNet | PVTv2-b4 | ImageNet-1K | 1x | No | 46.1 | - |
| RetinaNet | PVTv2-b5 | ImageNet-1K | 1x | No | 46.2 | - |
| Mask R-CNN | PVTv2-b0 | ImageNet-1K | 1x | No | 38.2 | 36.2 |
| Mask R-CNN | PVTv2-b1 | ImageNet-1K | 1x | No | 41.8 | 38.8 |
| Mask R-CNN | PVTv2-b2 | ImageNet-1K | 1x | No | 45.3 | 41.2 |
| Mask R-CNN | PVTv2-b3 | ImageNet-1K | 1x | No | 47.0 | 42.5 |
| Mask R-CNN | PVTv2-b4 | ImageNet-1K | 1x | No | 47.5 | 42.7 |
| Mask R-CNN | PVTv2-b5 | ImageNet-1K | 1x | No | 47.4 | 42.5 |
| Method | Backbone | Pretrain | Lr schd | Aug | box AP | mask AP |
|---|---|---|---|---|---|---|
| Cascade Mask R-CNN | PVTv2-b2-Linear | ImageNet-1K | 3x | Yes | 50.9 | 44.0 |
| Cascade Mask R-CNN | PVTv2-b2 | ImageNet-1K | 3x | Yes | 51.1 | 44.4 |
| ATSS | PVTv2-b2-Linear | ImageNet-1K | 3x | Yes | 48.9 | - |
| ATSS | PVTv2-b2 | ImageNet-1K | 3x | Yes | 49.9 | - |
| GFL | PVTv2-b2-Linear | ImageNet-1K | 3x | Yes | 49.2 | - |
| GFL | PVTv2-b2 | ImageNet-1K | 3x | Yes | 50.2 | - |
| Sparse R-CNN | PVTv2-b2-Linear | ImageNet-1K | 3x | Yes | 48.9 | - |
| Sparse R-CNN | PVTv2-b2 | ImageNet-1K | 3x | Yes | 50.1 | - |
- PVTv1 on COCO
| Detector | Backbone | Pretrain | Lr schd | box AP | mask AP |
|---|---|---|---|---|---|
| RetinaNet | PVT-Tiny | ImageNet-1K | 1x | 36.7 | - |
| RetinaNet | PVT-Small | ImageNet-1K | 1x | 40.4 | - |
| Mask RCNN | PVT-Tiny | ImageNet-1K | 1x | 36.7 | 35.1 |
| Mask RCNN | PVT-Small | ImageNet-1K | 1x | 40.4 | 37.8 |
| DETR | PVT-Small | ImageNet-1K | 50ep | 34.7 | - |
Segmentation configs & weights see >>>here<<<.
PVT-v2 + Segmentation see >>>here<<<.
- PVTv1 on ADE20K
| Method | Backbone | Pretrain | Iters | mIoU |
|---|---|---|---|---|
| Semantic FPN | PVT-Tiny | ImageNet-1K | 40K | 35.7 |
| Semantic FPN | PVT-Small | ImageNet-1K | 40K | 39.8 |
| Semantic FPN | PVT-Medium | ImageNet-1K | 40K | 41.6 |
| Semantic FPN | PVT-Large | ImageNet-1K | 40K | 42.1 |
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers. pdf | code
Masked Vision-Language Transformer in Fashion. pdf | code
This repository is released under the Apache 2.0 license as found in the LICENSE file.
If you use this code for a paper, please cite:
PVTv1
@inproceedings{wang2021pyramid,
title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={568--578},
year={2021}
}
PVTv2
@article{wang2021pvtv2,
title={Pvtv2: Improved baselines with pyramid vision transformer},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
journal={Computational Visual Media},
volume={8},
number={3},
pages={1--10},
year={2022},
publisher={Springer}
}
This repo is currently maintained by Wenhai Wang (@whai362), Enze Xie (@xieenze), and Zhe Chen (@czczup).