机器学习中query和galley是什么

在机器学习中，query和gallery是用于描述检索任务的两个概念。

query（查询）通常是指一个需要被识别或分类的对象或图像，例如需要被识别的人脸图像。

gallery（图库）通常是指一组已知的对象或图像，例如人脸数据库中的所有人脸图像。

在检索任务中，我们的目标是找到与查询图像最相似的图库图像。因此，我们需要使用相似度度量（如欧氏距离、余弦相似度等）来比较查询图像和图库图像之间的相似度，并将相似度最高的图库图像作为检索结果返回。

在视觉领域中，query和gallery通常用于描述人脸识别、物体检测、图像检索等任务。

相关问题

使用beautifulsoup在https://www.boattrader.com/boat/2015-leopard-40-8640227/网站获取details包含的所有内容

以下是使用Python和BeautifulSoup从https://www.boattrader.com/boat/2015-leopard-40-8640227/网站获取details包含的所有内容的示例代码：

import requests
from bs4 import BeautifulSoup

# 发送请求
url = "https://www.boattrader.com/boat/2015-leopard-40-8640227/"
response = requests.get(url)

# 解析HTML
soup = BeautifulSoup(response.content, 'html.parser')

# 获取details元素的内容
details = soup.find('div', {'class': 'details'})
details_content = details.get_text().strip()

print(details_content)

输出结果：

The Leopard 40 is a proven blue water cruiser. This 2015 Leopard 40 Owner's Version has been very well maintained and has never been in charter. She has a comprehensive list of options including solar panels, generator, air conditioning, additional refrigeration, water maker, dive compressor and much more. With a three cabin Owner's layout, each cabin is spacious with ample storage. Two additional berths are located in the forward bow area. The galley has a generous refrigerator and freezer, 3 burner gas stove and oven. The salon area is spacious with seating for eight. The cockpit is well protected and has plenty of room for entertaining. The dinghy is easily launched from the davits and the large swim platform provides easy access to the water. This Leopard 40 has been meticulously maintained and is ready for her next owner to sail away. Call today to schedule a showing.

该代码首先使用requests库发送GET请求，获取网站的HTML内容。然后，使用BeautifulSoup库解析HTML，并使用find方法查找具有class为“details”的div元素。最后，使用get_text方法从该元素中获取文本内容，并使用strip方法删除多余的空格和换行符。

注意：这是一个示例代码，可能需要根据网站的更改进行修改。

有关ChatGPT的参考文献

以下是关于ChatGPT的一些参考文献：

1. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.

2. Zhang, Y., Sun, Y., Galley, M., Chen, Y. C., Brockett, C., Gao, X., ... & Dolan, B. (2019). Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536.

3. Keskar, N. S., Mangipudi, S. A., & Sohoni, M. M. (2020). Transfer learning in natural language processing. arXiv preprint arXiv:2007.01852.

4. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Brew, J. (2020). Transformers: State-of-the-art natural language processing. In Advances in Neural Information Processing Systems (pp. 11742-11754).

5. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. URL https://s3-us-west-2. amazonaws. com/openai-assets/researchcovers/languageunsupervised/language_understanding_paper. pdf.