Open sxf97 opened 1 year ago
You can follow the instructions in https://github.com/jiasenlu/vilbert_beta/tree/master/data. You just need to modify the code in file ./tools/generate_tsv.py. Specifically, change "image_ids" to the list of image paths you want to extract features. If you only want to use the image features of MABSA downstream datasets, we provide the features in google drive https://drive.google.com/drive/folders/1rm0FtHOTMUfZfRjWIE9Ukn_1D5MDXQy3?usp=sharing.
好的,谢谢你呀
你好,我还想问一下这个代码的运行,您是在windows下还是在linux、ubuntu下运行的,caffa环境搭建好难
是在linux ubuntu环境下的,caffe配置是比较麻烦
You can follow the instructions in https://github.com/jiasenlu/vilbert_beta/tree/master/data. You just need to modify the code in file ./tools/generate_tsv.py. Specifically, change "image_ids" to the list of image paths you want to extract features. If you only want to use the image features of MABSA downstream datasets, we provide the features in google drive https://drive.google.com/drive/folders/1rm0FtHOTMUfZfRjWIE9Ukn_1D5MDXQy3?usp=sharing.
请问后面预训练所需要的 _box
_att
和 _cls_again
文件夹下的 .npy
和 .npz
文件都是通过这里得到的 .tsv
转换而来的吗?
是的
I would like to know how to use Faster-RCNN to extract the region feature(only retain 36 regions with highest Confidence) as the input feature and the dimension of each region feature is 2048,Can you give me a small demo if possible?