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---
license: mit
datasets:
- coco
- openimagesv5
- objects365v1
- visualgenome
library_name: pytorch
tags:
- pytorch
- feature-extraction
---
# Model Card: VinVL VisualBackbone
Disclaimer: The model is taken from the official repository, it can be found here: [microsoft/scene_graph_benchmark](https://github.com/microsoft/scene_graph_benchmark)
# Usage:
More info about how to use this model can be found here: [michelecafagna26/vinvl-visualbackbone](https://github.com/michelecafagna26/vinvl-visualbackbone)
# Quick start: Feature extraction
```python
from scene_graph_benchmark.wrappers import VinVLVisualBackbone
img_file = "scene_graph_bechmark/demo/woman_fish.jpg"
detector = VinVLVisualBackbone()
dets = detector(img_file)
```
`dets` contains the following keys: ["boxes", "classes", "scores", "features", "spatial_features"]
You can obtain the full VinVL's visual features by concatenating the "features" and the "spatial_features"
```python
import numpy as np
v_feats = np.concatenate((dets['features'], dets['spatial_features']), axis=1)
# v_feats.shape = (num_boxes, 2054)
```
# Citations
Please consider citing the original project and the VinVL paper
```BibTeX
@misc{han2021image,
title={Image Scene Graph Generation (SGG) Benchmark},
author={Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Jianfeng Gao and Pengchuan Zhang},
year={2021},
eprint={2107.12604},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@inproceedings{zhang2021vinvl,
title={Vinvl: Revisiting visual representations in vision-language models},
author={Zhang, Pengchuan and Li, Xiujun and Hu, Xiaowei and Yang, Jianwei and Zhang, Lei and Wang, Lijuan and Choi, Yejin and Gao, Jianfeng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5579--5588},
year={2021}
}
```
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