Update README.md

README.md

@@ -1,3 +1,70 @@
 ---
 license: mit
+
+datasets:
+- coco
+- openimagesv5
+- bbjects365v1
+- visualgenome
+
+library_name: pytorch
+tags:
+- pytorch
 ---
+
+# 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}
+}
+
+```