Spacenet Challenge 5

https://spacenet.ai/sn5-challenge/

The Machine Learning Reproducibility Checklist

The Machine Learning Reproducibility Checklist  
 
https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf


For all models and algorithms presented, check if you include:

• A clear description of the mathematical setting, algorithm, and/or model. 
•  An analysis of the complexity (time, space, sample size) of any algorithm. 
• A link to a downloadable source code, with specification of all dependencies, including external libraries.

For any theoretical claim, check if you include:

• A statement of the result.
• A clear explanation of any assumptions.
• A complete proof of the claim.  

For all figures and tables that present empirical results, check if you include: 

• A complete description of the data collection process, including sample size.  
• A link to a downloadable version of the dataset or simulation environment. An explanation of any data that were excluded, description of any pre-processing step. 
• An explanation of how samples were allocated for training / validation / testing. 
• The range of hyper-parameters considered, method to select the best hyper-parameter configuration, and specification of all hyper-parameters used to generate results. 
• The exact number of evaluation runs. 
• A description of how experiments were run. 
• A clear definition of the specific measure or statistics used to report results. 
• Clearly defined error bars. 
• A description of results with central tendency(e.g. mean) & variation(e.g. std dev).  
• A description of the computing infrastructure used