EfficientNet-PyTorch
BIOBSS
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EfficientNet-PyTorch | BIOBSS | |
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2 | 1 | |
7,715 | 94 | |
- | - | |
0.0 | 0.6 | |
about 2 years ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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EfficientNet-PyTorch
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[D] MCDropout and CNNs
I used this with the popular pytorch implementation of EfficientNet. You can see what I'm talking about here https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py on line 127. Once you understand this code it is pretty straightforward to modify your forward pass to allow "stochastic depth" during inference.
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[P] Backprop: a library to easily finetune and use state-of-the-art models
I dont see you credit the author of https://github.com/lukemelas/EfficientNet-PyTorch yet you're using his implementation for efficientnet.
BIOBSS
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Open source biomedical signal processing library BIOBSS, focused on ease of use, low code and, easy access to commonly used features.
Our team at our company (OBSS Technology) was working on a biomedical signal processing project. https://github.com/obss/BIOBSS
What are some alternatives?
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
NeuroKit - NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
MLclf - mini-imagenet and tiny-imagent dataset transformation for traditional classification task and also for the format for few-shot learning / meta-learning tasks
PulseSensorPlayground - A PulseSensor library (for Arduino) that collects our most popular projects in one place.
kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
DropoutUncertaintyExps - Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
hrv-analysis - Package for Heart Rate Variability analysis in Python