Update README.md

DaWiiX · Mar 29, 2022 · 5ac4525 · 5ac4525
1 parent e7e9d8b
commit 5ac4525
Showing 1 changed file with 5 additions and 100 deletions.
diff --git a/README.md b/README.md
@@ -1,45 +1,10 @@
 # BOAT: Bilateral Local Attention Vision Transformer
 
 
-This is an unofficial implementation of the paper
-We only change ./model/swin_transformer.py to ./model/boat_swin_transformer.py and keep other codes unchanged.
-
-
-# Start
-
-Please refer to [Start for Swin](https://github.com/microsoft/Swin-Transformer/blob/main/get_started.md) for installing the prerequisite.
-
-# Training
-
-`BOAT-Swin-T`:
-
-```bash
-python -m torch.distributed.launch --nproc_per_node 4 --master_port 12345 main.py \
---cfg configs/swin_tiny_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 256
-```
-
-`BOAT-Swin-S`:
-
-```bash
-python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
---cfg configs/swin_small_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 128 
-```
-
-`BOAT-Swin-B`:
-
-```bash
-python -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py \
---cfg configs/swin_base_patch4_window7_224.yaml --data-path <imagenet-path> --batch-size 128 \
-```
-
-# Evaluation
-
-To evaluate a pre-trained `BOAT-Swin Transformer` on ImageNet val, run:
-
-```bash
-python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
---cfg <config-file> --resume <checkpoint> --data-path <imagenet-path> 
-```
+This is an unofficial implementation of the paper BOAT: Bilateral Local Attention Vision Transformer. 
+The [Swin variant](https://github.com/mahaoyuHKU/pytorch-boat/tree/main/Swin) is based on [Swin Transformer](https://github.com/microsoft/Swin-Transformer)
+The [CSwin variant](https://github.com/mahaoyuHKU/pytorch-boat/tree/main/CSwin) is based on [CSwin Tranformer](https://github.com/microsoft/CSWin-Transformer)
+Please check corresponding folders for more installation instructions.
 
 # Pre-trained models
 
@@ -54,69 +19,9 @@ python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --maste
 [BOAT-CSwin-Small](https://www.dropbox.com/s/cnl00d1faxxoi19/cswin_small.pth.tar?dl=0)
 
 [BOAT-CSwin-Base](https://www.dropbox.com/s/92sr8r8zhng1mqg/cswin_base.pth.tar?dl=0)
-# CSWin-Transformer
-
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/cswin-transformer-a-general-vision/semantic-segmentation-on-ade20k)](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=cswin-transformer-a-general-vision)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/cswin-transformer-a-general-vision/semantic-segmentation-on-ade20k-val)](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val?p=cswin-transformer-a-general-vision)
-
-This repo is the official implementation of ["CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows"](https://arxiv.org/pdf/2107.00652.pdf). 
-
-## Introduction
-
-
-## Requirements
-
-timm==0.3.4, pytorch>=1.4, opencv, ... , run:
-
-```
-bash install_req.sh
-```
-
-Apex for mixed precision training is used for finetuning. 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" ./
-```
-
-Data prepare: ImageNet with the following folder structure, you can extract imagenet by this [script](https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4).
-
-```
-│imagenet/
-├──train/
-│ ├── n01440764
-│ │ ├── n01440764_10026.JPEG
-│ │ ├── n01440764_10027.JPEG
-│ │ ├── ......
-│ ├── ......
-├──val/
-│ ├── n01440764
-│ │ ├── ILSVRC2012_val_00000293.JPEG
-│ │ ├── ILSVRC2012_val_00002138.JPEG
-│ │ ├── ......
-│ ├── ......
-```
-
-## Train
-
-Train the three lite variants: CSWin-Tiny, CSWin-Small and CSWin-Base:
-```
-bash train.sh 8 --data <data path> --model CSWin_64_12211_tiny_224 -b 256 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99984 --drop-path 0.2
-```
-```
-bash train.sh 8 --data <data path> --model CSWin_64_24322_small_224 -b 256 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99984 --drop-path 0.4
-```
-```
-bash train.sh 8 --data <data path> --model CSWin_96_24322_base_224 -b 128 --lr 1e-3 --weight-decay .1 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99992 --drop-path 0.5
-```
-
-
-
-
 
 ## Acknowledgement
-This is developped based on CSWin Transformer
+This is developped based on CSWin Transformer and Swin-transformer
 
 
 # If you use this code for your research, please consider citing: