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https://github.com/jin-s13/gkgnet
ECCV'2024 "GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition"
https://github.com/jin-s13/gkgnet
Last synced: 3 months ago
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ECCV'2024 "GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition"
- Host: GitHub
- URL: https://github.com/jin-s13/gkgnet
- Owner: jin-s13
- Created: 2024-07-08T03:09:13.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-08-31T15:56:30.000Z (6 months ago)
- Last Synced: 2024-10-19T16:33:59.108Z (4 months ago)
- Language: Python
- Homepage:
- Size: 8.9 MB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# GKGNet
[](https://paperswithcode.com/sota/multi-label-classification-on-pascal-voc-2007?p=gkgnet-group-k-nearest-neighbor-based-graph)
[](https://paperswithcode.com/sota/multi-label-classification-on-ms-coco?p=gkgnet-group-k-nearest-neighbor-based-graph)
## Introduction
This repo contains the official PyTorch implementation of our ECCV'2024 paper
[GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition](https://arxiv.org/abs/2308.14378).
## Quick Start Guide
### 1. Clone the Repository
To get started, clone the repository using the following commands:
```sh
git clone https://github.com/jin-s13/GKGNet.git
cd GKGNet
```
### 2. Environment Setup
Set up the required environment with the following commands:
```sh
conda create -n mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate mmlab
pip install openmim
mim install mmcv-full==1.5.0
pip install -e .
```
### 3. Data Preparation
Prepare the required data by downloading the [MS-COCO 2014](https://cocodataset.org/#download) dataset. The file structure should look like this:
```sh
-0data
-coco
-train.data
-val_test.data
-annotations
-instances_train2014.json
-instances_val2014.json
-train2014
-COCO_train2014_000000000009.jpg
-COCO_train2014_000000000025.jpg
...
-val2014
-COCO_val2014_000000000042.jpg
-COCO_val2014_000000000073.jpg
...
-GKGNet
-configs
-checkpoint
-pvig_s_82.1.pth.tar
-tools
...
```
You can obtain `train.data` and `val_test.data` from the `coco_multi_label_annos` directory. The pretrained backbones on ImageNet can be downloaded from [Vig](https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/pyramid-vig/pvig_s_82.1.pth.tar):
```sh
mkdir checkpoint
wget https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/pyramid-vig/pvig_s_82.1.pth.tar
mv pvig_s_82.1.pth.tar checkpoint/
```
### 4. Training
To train the model, use one of the following commands:
#### Single Process
```sh
python tools/train.py configs/gkgnet/gkgnet_coco_576.py
```
#### Multi Process
```sh
bash tools/dist_train.sh configs/gkgnet/gkgnet_coco_576.py 8
```
### 5. Pretrained Models
#### 5.1 Download Pretrained Models
You can download the pretrained models from the following link:
| Model Name | mAP | Link (Google Drive) |
| -------------| ---- | ---------------------|
| GKGNet-576 | 87.65| [Download Link](https://drive.google.com/file/d/1TB_UqqFvpQ2bvy_qau0aKP6GoK9Xlix_/view?usp=share_link) |
#### 5.2 Test Pretrained Models
To test the pretrained models, run the following command:
```sh
python tools/test.py configs/gkgnet/gkgnet_coco_576.py *.pth --metrics mAP
or
bash tools/dist_test.sh configs/gkgnet/gkgnet_coco_576.py *.pth 8 --metrics mAP
```
## Acknowledgement
This repo is developed based on [MMPreTrain](https://github.com/open-mmlab/mmpretrain).
## License
GKGNet is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please contact Mr. Sheng Jin (jinsheng13[at]foxmail[dot]com). We will send the detail agreement to you.