Flask is a microframework for Python based on Werkzeug, Jinja 2 and good intentions. And before you ask: It's BSD licensed! flask.pocoo.org/

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Keras作者François Chollet在Twitter上推荐了Sis,一个基于Keras和Flask的开源图像搜索引擎。

Github项目地址: https://github.com/matsui528/sis

启动Sis仅需要运行下面两个Python脚本:

  • offline.py: 该脚本从图像中提取深度特征。指定一个图像数据库,它会用在ImageNet上预训练的VGG16网络为每张图像提取4096维的特征。
  • server.py: 该脚本运行Web服务。你可以用Flask的Web接口将请求图像发送给服务,服务会用简单的最近邻搜索方法来检索相关的图像。

在一台有t2.large的aws-ec2实例中, 每张图片的特征提取消耗0.9秒,搜索1000张图片耗时10毫秒。Sis在有Python3环境的Ubuntu 16.04上测试过。

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WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian-synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and uncertainties on non-fossil-fuel CO$_2$ fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019, Atmos. Chem. Phys., vol. 19). We also find that our predictions of out-of-sample retrievals from the Total Column Carbon Observing Network are, for the most part, more accurate than those made by the MIP participants. Subsequent versions of the OCO-2 datasets will be ingested into WOMBAT as they become available.

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