Awesome

AS-MLP for Object Detection

This repo contains the supported code and configuration files to reproduce object detection results of AS-MLP. It is based on Swin Transformer.

Results and Models

Mask R-CNN

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

Cascade Mask R-CNN

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.

Usage

Installation

Please refer to get_started.md for installation and dataset preparation.

Inference

# 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

Training

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.

Apex (optional):

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" ./

Citation

@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}
}