Vinvl Base Image Captioning

🚀 用于图像描述的VinVL模型卡片 🖼️

微软的VinVL 基础预训练模型，专为图像描述生成下游任务而设计。该模型能有效处理图像并生成准确的文字描述，为图像理解和信息传递提供了强大的支持。

🚀 快速开始

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"

# 原始代码
config = BertConfig.from_pretrained(ckpt)
tokenizer = BertTokenizer.from_pretrained(ckpt)
model = BertForImageCaptioning.from_pretrained(ckpt, config=config).to(device)

# 负责预处理
tensorizer = OscarTensorizer(tokenizer=tokenizer, device=device)

# 形状为 (1, num_boxes, feat_size) 的numpy数组
# 在VinVL中，feat_size默认是2054
visual_features = torch.from_numpy(feat_obj).to(device).unsqueeze(0)

# 标签通常由特征提取器提取
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)

# 输出如下所示
# pred = {0: [{'caption': 'a red and white boat traveling down a river next to a small boat.', 'conf': 0.7070220112800598]}

✨ 主要特性

• 该模型有一个独立的视觉骨干网络用于特征提取。

📦 安装指南

关于如何安装和使用此模型的更多信息，请参考：michelecafagna26/VinVL

💻 使用示例

基础用法

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"

# 原始代码
config = BertConfig.from_pretrained(ckpt)
tokenizer = BertTokenizer.from_pretrained(ckpt)
model = BertForImageCaptioning.from_pretrained(ckpt, config=config).to(device)

# 负责预处理
tensorizer = OscarTensorizer(tokenizer=tokenizer, device=device)

# 形状为 (1, num_boxes, feat_size) 的numpy数组
# 在VinVL中，feat_size默认是2054
visual_features = torch.from_numpy(feat_obj).to(device).unsqueeze(0)

# 标签通常由特征提取器提取
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)

# 输出如下所示
# pred = {0: [{'caption': 'a red and white boat traveling down a river next to a small boat.', 'conf': 0.7070220112800598]}

📚 详细文档

COCO测试集指标 📈

作者提供的表格（表7，交叉熵优化）

特征提取 ⛏️

此模型有一个独立的视觉骨干网络用于特征提取。

更多关于以下内容的信息：

• 模型：michelecafagna26/vinvl_vg_x152c4
• 使用和安装：michelecafagna26/vinvl-visualbackbone

数据集

库信息

📄 许可证

本模型采用Apache-2.0许可证。

🧾 引用

请考虑引用原始项目和VinVL论文

@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}
}