Difference between revisions of "User:Bralani/GSoC2024/Log"

From BRL-CAD
(Development Logs)
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== Monday, July 8st, 2024==
 
== Monday, July 8st, 2024==
 
Tried with different optimizers, results improved to 99.1% with Nesterov Adam that converges faster and it is very stable between epochs.
 
Tried with different optimizers, results improved to 99.1% with Nesterov Adam that converges faster and it is very stable between epochs.
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== Tuesday, July 9st, 2024==
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Started organizing source code to better understand the workflow.

Revision as of 14:34, 9 July 2024

Development Logs

Community Bonding Period


Monday, July 1st, 2024

Downloaded the code from the repo of fall rainy in order to have a common base source code. Then, installed different libraries (like pytorch) to make it work.

Tuesday, July 2st, 2024

Implemented a different version of the neural network (NIF) to make it work with hit/miss task. Got a 0.99 accuracy with bounding sphere approach in the training set.

Wednesday, July 3st, 2024

Compared a normal rendering with the neural network (NIF) one: results are still far from being acceptable.

Thursday, July 4st, 2024

Got an average accuracy of 50% with NIF in predicting the true rendering. Tried with a simple KNN with a billion rays stored, got always 50% of accuracy. -> This means that there are some errors in the pipeline (maybe in generating the rays or conversions in spherical coordinates) because the KNN should be better than the random classifier with a billion rays of training set.

Friday, July 5st, 2024

Solved issues with rendering, got a maximum accuracy of 98.6% with the true rendering (bounding sphere sampling). Pay attention that this result has been achieved only with a single render.

Saturday, July 6st, 2024

Implemented a benchmark function that generate different angles of the object (test set). Implemented a different version of loss function (symmetric). Got a maximum accuracy of 98.8% and this result is unbiased -> high confidence.

Sunday, July 7st, 2024

Implemented a grid encoding approach => results are still of the order of 98.8%.

Monday, July 8st, 2024

Tried with different optimizers, results improved to 99.1% with Nesterov Adam that converges faster and it is very stable between epochs.

Tuesday, July 9st, 2024

Started organizing source code to better understand the workflow.