Difference between revisions of "User:Bralani/GSoC2024/Log"
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==Tuesday, July 2st, 2024== | ==Tuesday, July 2st, 2024== | ||
| − | Implemented a different version of the neural network (NIF) to make it work with hit/miss task. Got a 0. | + | Implemented a different version of the neural network (NIF) to make it work with hit/miss task. Got a 0.98 accuracy with bounding sphere approach. |
==Wednesday, July 3st, 2024== | ==Wednesday, July 3st, 2024== | ||
Compared a normal rendering with the neural network (NIF) one: results are still far from being acceptable. | Compared a normal rendering with the neural network (NIF) one: results are still far from being acceptable. | ||
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| + | ==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. | ||
Revision as of 04:23, 5 July 2024
Contents
Development Logs
Community Bonding Period
- Familiarizing with previous work,especially [Neural Intersection Functions](https://arxiv.org/abs/2306.07191)
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 0.98 accuracy with bounding sphere approach.
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.
