ADE20k Semantic segmentation with MAE
Getting started
- Install the mmsegmentation library and some required packages.
pip install mmcv-full==1.3.0 mmsegmentation==0.11.0
pip install scipy timm==0.3.2- Install apex for mixed-precision training
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" ./- Follow the guide in mmseg to prepare the ADE20k dataset.
Fine-tuning for Reproducing Results of MAE ViT-Base
Command:
tools/dist_train.sh configs/mae/upernet_mae_base_12_512_slide_160k_ade20k.py 8 --seed 0 --options model.pretrained=https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth
Expected results log(paper results: 48.1 mIoU):
+--------+-------+-------+-------+
| Scope | mIoU | mAcc | aAcc |
+--------+-------+-------+-------+
| global | 48.15 | 58.99 | 83.05 |
+--------+-------+-------+-------+
Evaluation
Command format:
tools/dist_test.sh <CONFIG_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval mIoU
Acknowledgment
This code is built using the mmsegmentation library, Timm library, the Swin repository, XCiT, SETR, BEiT and the MAE repository.