This repo contains the supported code and configuration files to reproduce object detection results of AS-MLP. It is based on Swin Transformer.
| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | Params | FLOPs | config | model |
|---|---|---|---|---|---|---|---|---|
| AS-MLP-T | ImageNet-1K | 1x | 44.0 | 40.0 | 48M | 260G | config | onedrive |
| AS-MLP-T | ImageNet-1K | 3x | 46.0 | 41.5 | 48M | 260G | config | |
| AS-MLP-S | ImageNet-1K | 1x | 46.7 | 42.0 | 69M | 346G | config | |
| AS-MLP-S | ImageNet-1K | 3x | 47.8 | 42.9 | 69M | 346G | config |
| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | Params | FLOPs | config | model |
|---|---|---|---|---|---|---|---|---|
| AS-MLP-T | ImageNet-1K | 1x | 48.4 | 42.0 | 86M | 739G | config | onedrive |
| AS-MLP-T | ImageNet-1K | 3x | 50.1 | 43.5 | 86M | 739G | config | |
| AS-MLP-S | ImageNet-1K | 1x | 50.5 | 43.7 | 107M | 824G | config | |
| AS-MLP-S | ImageNet-1K | 3x | 51.1 | 44.2 | 107M | 824G | config | |
| AS-MLP-B | ImageNet-1K | 1x | 51.1 | 44.2 | 145M | 961G | config | |
| AS-MLP-B | ImageNet-1K | 3x | 51.5 | 44.7 | 145M | 961G | config |
Notes:
- Pre-trained models can be downloaded from AS-MLP for ImageNet Classification.
Please refer to get_started.md for installation and dataset preparation.
# single-gpu testing
python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox segm
# multi-gpu testing
tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval bbox segm
To train a detector with pre-trained models, run:
# single-gpu training
python tools/train.py <CONFIG_FILE> --cfg-options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]
# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --cfg-options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]
For example, to train a Mask R-CNN model with a AS-MLP-T backbone and 8 gpus, run:
tools/dist_train.sh configs/asmlp/mask_rcnn_asmlp_tiny_patch4_shift5_mstrain_480-800_adamw_3x_coco.py 8 --cfg-options model.pretrained=<PRETRAIN_MODEL>
Note: use_checkpoint is used to save GPU memory. Please refer to this page for more details.
We use apex for mixed precision training by default. To install apex, run:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
@article{Lian_2021_ASMLP,
author = {Lian, Dongze and Yu, Zehao and Sun, Xing and Gao, Shenghua},
title = {AS-MLP: An Axial Shifted MLP Architecture for Vision},
journal={ICLR},
year = {2022}
}
Image Classification: See AS-MLP for Image Classification.