Implementation of EfficientNet: Rethinking Model Scaling for CNNs
Developed by Team Cygnus in SRM MIC Rescon 1.0
Click »here« for the original paper
Click »here« for the presentation
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, the Efficient Net-B6-Wide achieves state-of-the-art 91.12% top-1 accuracy on ImageNet(480M Parameters), while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. The empirical research on EfficientNets shows that it is critical to balance all dimensions of network width/ depth/ resolution, and surprisingly such balance can be achieved by simply scaling each of them with constant ratio.How does Compound Scaling work and is different from old paradigms
- Scaling up any dimension of network width, depth, or resolution improves accuracy, but the accuracy gain diminishes for bigger models.
- In order to pursue better accuracy and efficiency, it is critical to balance all dimensions of network width, depth, and resolution during ConvNet scaling. Intuitively, the compound scaling method makes sense because if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image
How Efficient net performs when compared to past transfer learning models.
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Fork the project
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Open Terminal in your desired folder
git clone https://github.com/sd2001/EfficientNet.git
cd EfficientNet
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
cd src
python architecture.py
To check the Notebooks
- In the Efficient Folder
jupyter notebook
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