EfficientNet-PyTorch
PyTorch-Model-Compare
| EfficientNet-PyTorch | PyTorch-Model-Compare | |
|---|---|---|
| 2 | 3 | |
| 7,715 | 316 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | about 1 year 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.
PyTorch-Model-Compare
What are some alternatives?
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
EfficientFormer - EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
BIOBSS - A package for processing signals recorded using wearable sensors, such as Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activity (EDA) and 3-axis acceleration (ACC).
mapextrackt - Pytorch Feature Map Extractor
MLclf - mini-imagenet and tiny-imagent dataset transformation for traditional classification task and also for the format for few-shot learning / meta-learning tasks
pytorch2keras - PyTorch to Keras model convertor
kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
DropoutUncertaintyExps - Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"