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License: GNU General Public License v3.0
An Automated Image Quality and Label Tagger for Photography Workflows
License: GNU General Public License v3.0
Increase training accuracy by adding negative labels.
Someone removes a label from an image, that is a confirmed negative label.
Extra Reading:
https://arxiv.org/abs/1908.07387
https://github.com/ydkim1293/NLNL-Negative-Learning-for-Noisy-Labels/blob/master/main_NL.py
Goals:
The goal of this issue is to address issues that prevent each process from running continuously.
Currently during training, we are loading all the images into memory. This could cause severe memory problems.
Remove epochs during training; allow learning to progress until a accuracy deceleration is hit on validation data.
Currently, we are using resnet 50 that was pre-trained and perform transfer learning on the fully connected portion of the network.
We freeze ALL layers except the fully connected layers at the end of the network.
To improve performance we can cache the results before they are passed through the fully connected layers.
During training we can train directly from the
feature map -> fully connected -> outputs.
This is opposed to the current design of
thumbnail -> convolutions -> feature map -> fully connected -> outputs
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