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An official codebase of Scene-Aware Label Graph Learning for Multi-Label Image Classification, ICCV 2023.

SALGL 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Scene-Aware Label Graph Learning for Multi-Label Image Classification

### Introduction
This is an official PyTorch implementation of Scene-Aware Label Graph Learning for Multi-Label Image Classification, ICCV 2023. [[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhu_Scene-Aware_Label_Graph_Learning_for_Multi-Label_Image_Classification_ICCV_2023_paper.pdf)

![alt tsformer](https://raw.githubusercontent.com/jasonseu/SALGL/main/src/framework.png)

### Requirements
```
torch >= 1.9.0
torchvision >= 0.10.0
```

### Evaluation
Pre-trained weights can be found in [google drive](https://drive.google.com/drive/folders/1Ys3c5a19Mg_PguDn5F2fKKnAFNx93uLX?usp=sharing). Download and put them in the `experiments` folder, then one can use following commands to reproduce results reported in paper.
```bash
python evaluate.py --exp-dir experiments/salgl_resnet101_mscoco/exp3     # Microsoft COCO (448 x 448)
python evaluate.py --exp-dir experiments/salgl_resnet101_mscoco/exp6     # Microsoft COCO (576 x 576)
python evaluate.py --exp-dir experiments/salgl_resnet101_nuswide/exp2    # NUS-WIDE
python evaluate.py --exp-dir experiments/salgl_vit_large_patch16_224_mscoco/exp1     # Pascal VOC 2007
```

### Visualization
To visualize the word cloud and the label co-occurrence probability heatmap of different scene categories, please first download coco experiments (salgl_resnet101_mscoco) and then run following commands:
```bash
python labelcloud.py --exp-dir experiments/salgl_resnet101_mscoco/exp3
python heatmap.py --exp-dir experiments/salgl_resnet101_mscoco/exp3
```

### Citation
```
@inproceedings{zhu2023scene,
  title={Scene-aware label graph learning for multi-label image classification},
  author={Zhu, Xuelin and Liu, Jian and Liu, Weijia and Ge, Jiawei and Liu, Bo and Cao, Jiuxin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1473--1482},
  year={2023}
}
```

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