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# US/Pacific 7/7/2014 | ||
# US/Pacific 7/23/2014 | ||
= Deploy web app | ||
= Write dummy web app for uploading a request and downloading the response | ||
= Finish crosscompute-scaffolds | ||
Write dummy queue that takes a request and returns a response | ||
Write dummy processor that takes a request and returns a response | ||
Replace dummy proccesor with real processor | ||
Feature Modi Research Group logo and link on tool webpage | ||
Improve development classifier | ||
Investigate why the positive count is 7027 instead of 7028 | ||
Consider optimizing get_counts by searching maximums locally | ||
Consider adding random caching to get_next_batch | ||
Consider making coarse classifier to optimize speed | ||
Experiment with training on test set | ||
Experiment with autoencoders to pretrain neural network | ||
Experiment with YUV colorspace | ||
Use cuda-convnet2 | ||
Combine Amazon Mechanical Turk results into a clean dataset [8h] | ||
Set up Amazon Mechanical Turk to estimate the building count in a window [10h] | ||
Make scripts easier to use | ||
Let options.cfg read environment variables | ||
Send access credentials to all tools, valid with no membership fee for one year | ||
Add payment | ||
Experiment with message queues | ||
Mask paths in run.pkl | ||
Feature Modi Research Group logo and link on tool webpage | ||
Evaluate classifiers on cost-effective lower resolution satellite images | ||
Evaluate classifiers on cost-effective lower resolution satellite images |
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