wehub-resource-sync
83 lines
2.7 KiB
Markdown
83 lines
2.7 KiB
Markdown
# ADE20k Semantic segmentation with BEiT
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## Getting started
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1. Install the [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) library and some required packages.
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```bash
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pip install mmcv-full==1.3.0 mmsegmentation==0.11.0
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pip install scipy timm==0.3.2
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```
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2. Install [apex](https://github.com/NVIDIA/apex) for mixed-precision training
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```bash
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git clone https://github.com/NVIDIA/apex
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cd apex
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pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
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```
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3. Follow the guide in [mmseg](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k) to prepare the ADE20k dataset.
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## Fine-tuning
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Command format:
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```
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tools/dist_train.sh <CONFIG_PATH> <NUM_GPUS> --work-dir <SAVE_PATH> --seed 0 --deterministic --options model.pretrained=<IMAGENET_CHECKPOINT_PATH/URL>
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```
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For example, using a BEiT-base backbone with UperNet:
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```bash
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bash tools/dist_train.sh \
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configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_pt2ft.py 8 \
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--work-dir /path/to/save --seed 0 --deterministic \
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--options model.pretrained=https://github.com/addf400/files/releases/download/v1.0/beit_base_patch16_224_pt22k_ft22k.pth
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```
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More config files can be found at [`configs/beit/upernet`](configs/beit/upernet).
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## Evaluation
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Command format:
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```
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tools/dist_test.sh <CONFIG_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval mIoU
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```
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For example, evaluate a BEiT-base backbone with UperNet:
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```bash
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bash tools/dist_test.sh configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_pt2ft.py \
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https://github.com/addf400/files/releases/download/v1.0/beit_base_patch16_640_pt22k_ft22ktoade20k.pth 4 --eval mIoU
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```
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Expected results:
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```
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+--------+-------+-------+-------+
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| Scope | mIoU | mAcc | aAcc |
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+--------+-------+-------+-------+
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| global | 53.61 | 64.82 | 84.62 |
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+--------+-------+-------+-------+
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```
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Multi-scale + flip (`\*_ms.py`)
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```
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bash tools/dist_test.sh configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_ms.py \
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https://github.com/addf400/files/releases/download/v1.0/beit_base_patch16_640_pt22k_ft22ktoade20k.pth 4 --eval mIoU
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```
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Expected results:
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```
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+--------+-------+-------+------+
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| Scope | mIoU | mAcc | aAcc |
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+--------+-------+-------+------+
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| global | 54.26 | 65.28 | 84.9 |
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+--------+-------+-------+------+
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```
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---
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## Acknowledgment
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This code is built using the [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) library, [Timm](https://github.com/rwightman/pytorch-image-models) library, the [Swin](https://github.com/microsoft/Swin-Transformer) repository, [XCiT](https://github.com/facebookresearch/xcit) and the [SETR](https://github.com/fudan-zvg/SETR) repository.
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