用于特征提取对齐，选用输出为什么参数

[huainanchen]

import torch from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Image' model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') image_process = LanguageBindImageProcessor(model.config, tokenizer)

model.eval() data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

您好, 请问我如果加载LanguageBind_Image模型，用于图像和文本特征的提取对齐，那么我是用 out.text_embed和 out.image_embeds 这两个进行后续的工作吗？比如后续进行融合分类。

[LinB203]

[Chinese] 是的。 [En] Yes.