Assembly: Microsoft.VisualStudio.ImageCatalog.dll Package: Microsoft.VisualStudio.ImageCatalog v17.13.40008 C++ 複製 public: static property Microsoft::VisualStudio::Imaging::Interop::ImageMoniker ShowTemplateRegionLabel { Microsoft::VisualStudio::Imaging::Interop::ImageMoniker get(); }; Property V...
完成RegionCLIP文件的下载后,便需要配置RegionCLIP进行zero-shot推理的环境配置以及所需要的配置文件。 然后进行环境配置: !python -m pip install -e RegionCLIP 安装其他所需的文件: !pip install opencv-python timm diffdist h5py sklearn ftfy !pip install git+https://github.com/lvis-dataset/lvis-api.gi...
虽然“伪”区域-文本对存在噪声,但它们仍然为学习区域表示提供了有用的信息,从而弥合了与目标检测的差距,这已通过我们的实验得到验证。 我们在字幕数据集(_例如_Conceptual Caption)上预训练我们的模型,主要在开放词汇目标检测的基准测试(COCO和LVIS数据集)上评估模型。 当转移到开放词汇目标检测时,我们的预训练模型在...
Dataset and Checkpoints Training and Evaluation 6. ContactsIf you have any question about our work or this repository, please don't hesitate to contact us by emails or open an issue under this project.zhaoyuzhong20@mails.ucas.ac.cn liufeng20@mails.ucas.ac.cn wanfang@ucas.ac.cn7...
the report processor runs the query for each dataset in a report and the dataset query may produce no result set. For a data region bound to an empty dataset, you can specify text to display instead of displaying an empty data region. You can also set the NoRowsMessage property for a ...
!pip install git+https://github.com/lvis-dataset/lvis-api.git 数据集文件配置 这里需要进行其他数据集的配置: 首先需要下载一个pretrained_ckpt文件夹下到RegionCLIP文件夹下,其实这里可以不用下载,直接根据google云盘的共享,把文件共享到RegionCLIP文件夹下,具体操作如下图所示: ...
ReCo introduces position token embed- ding EP ∈ RNbins×D alongside the pre-trained text word while best pre3s5e9r4v5i0n1g20t1h7e, 'abpbopxe':a[l5i6n3g.7T, 62.3I9c, a61p.a7b, 2il0i.t5y6.], 'caption': 'chair', 'category_id': 6 'pad_caption': 'a green and white s...
2):RegionCLIP的预训练使用的是数据量为300万的(image-text pairs)Conceptual Caption dataset (CC3M)数据,为啥不使用CLIP模型所使用的数据量为4亿个image-caption数据?(即为啥不使用更大的数据量?若使用后,效果会如何?)如果我们想在大规模数据上做region clip的预训练可以怎么做?
Region-level captioning is challenged by the caption degeneration issue, which refers to that pre-trained multimodal models tend to predict the most frequent captions but miss the less frequent ones. In this study, we propose a controllable region-level captioning (ControlCap) approach, which ...
Datasets. For pretraining, we consider Conceptual Cap- tion dataset (CC3M) [45] with 3 millions of image-text pairs from the web. We also use a smaller dataset COCO Caption (COCO Cap) [7] when conducting ablation studies. COCO Cap contains 118k images, each associated with 5 ...