Update README.md

README.md

@@ -1,3 +1,115 @@
 ---
 license: apache-2.0
+datasets:
+- hl-scenes
+
+library_name: pytorch
+tags:
+- pytorch
+- image-to-text
 ---
+
+# Model Card: VinVL for Captioning 🖼️
+
+[Microsoft's VinVL](https://github.com/microsoft/Oscar) base fine-tuned on [HL-scenes]() dataset for **scene description generation** downstream task.
+
+# Model fine-tuning 🏋️‍
+
+The model has been finetuned for 10 epoch on [HL-scenes]() dataset
+
+# Test set metrics 📈
+
+Obtained with beam size 5 and max lenght 20
+
+| Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | METEOR | ROUGE-L | CIDEr | SPICE |
+|--------|--------|--------|--------|--------|---------|-------|-------|
+|  0.68  |  0.55  |  0.45  |  0.36  |  0.36  |  0.63   |  1.42 |  0.40 |
+
+
+# Usage and Installation:
+
+More info about how to install and use this model can be found here: [michelecafagna26/VinVL
+](https://github.com/michelecafagna26/VinVL)
+
+# Feature extraction ⛏️
+
+This model has a separate Visualbackbone used to extract features.
+More info about:
+- the model here: [michelecafagna26/vinvl_vg_x152c4](https://huggingface.co/michelecafagna26/vinvl_vg_x152c4)
+- the usage here [michelecafagna26/vinvl-visualbackbone](https://github.com/michelecafagna26/vinvl-visualbackbone)
+
+# Quick start: 🚀
+
+```python
+from transformers.pytorch_transformers import BertConfig, BertTokenizer
+from oscar.modeling.modeling_bert import BertForImageCaptioning
+from oscar.wrappers import OscarTensorizer
+
+ckpt = "path/to/the/checkpoint"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# original code
+config = BertConfig.from_pretrained(ckpt)
+tokenizer = BertTokenizer.from_pretrained(ckpt)
+model = BertForImageCaptioning.from_pretrained(ckpt, config=config).to(device)
+
+# This takes care of the preprocessing
+tensorizer = OscarTensorizer(tokenizer=tokenizer, device=device)
+
+# numpy-arrays with shape (1, num_boxes, feat_size)
+# feat_size is 2054 by default in VinVL
+visual_features = torch.from_numpy(feat_obj).to(device).unsqueeze(0)
+
+# labels are usually extracted by the features extractor
+labels = [['boat', 'boat', 'boat', 'bottom', 'bush', 'coat', 'deck', 'deck', 'deck', 'dock', 'hair', 'jacket']]
+
+inputs = tensorizer.encode(visual_features, labels=labels)
+outputs = model(**inputs)
+
+pred = tensorizer.decode(outputs)
+
+# the output looks like this:
+# pred = {0: [{'caption': 'in a library', 'conf': 0.7070220112800598]}
+```
+
+# Citations 🧾
+
+This is the model used in:
+
+```BibTeX
+@misc{cafagna2022understanding,
+  author = {Cafagna Michele and van Deemter Kees and Gatt Albert},
+  title = {Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions},
+  doi = {10.48550/ARXIV.2211.04971},
+  url = {https://arxiv.org/abs/2211.04971},
+  keywords = {Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+
+```
+
+
+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}
+}
+
+```