EfficientNet-B0: Image Classification
EfficientNet-B0 is the baseline model in the EfficientNet series introduced by Google in 2019. EfficientNet employs a "compound scaling" method, which jointly optimizes the network's depth, width, and resolution to achieve greater computational efficiency and accuracy. EfficientNet-B0 utilizes the Mobile Inverted Bottleneck Convolution (MBConv) module along with depthwise separable convolutions, significantly reducing parameters and computational cost. Despite being the smallest model in the series, EfficientNet-B0 performs excellently across various computer vision tasks, delivering high accuracy while maintaining low resource requirements, making it ideal for mobile devices and edge computing in resource-constrained environments.
Source model
- Input shape: 224x224
- Number of parameters: 5.04M
- Model size: 20.16M
- Output shape: 1x1000
Source model repository: EfficientNet-B0
Performance Reference
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License
Source Model: BSD-3-CLAUSE
Deployable Model: APLUX-MODEL-FARM-LICENSE
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