Shufflenet-v2: Image Classification
ShuffleNet-v2 is an efficient convolutional neural network designed for mobile and embedded devices. It introduces two key strategies: "channel splitting" and "channel shuffling" to optimize performance in resource-constrained environments. Channel splitting reduces computation, while channel shuffling ensures effective information exchange across different groups. Additionally, ShuffleNet-v2 simplifies the network structure, further reducing memory access cost and improving overall inference speed. This model is particularly suitable for tasks like image classification and object detection, significantly lowering computational complexity while maintaining high accuracy.
Source model
- Input shape: 224x224
- Number of parameters: 1.30M
- Model size: 5.26M
- Output shape: 1x1000
Source model repository: Shufflenet-v2
Performance Reference
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Inference & Model Conversion
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License
Source Model: BSD-3-CLAUSE
Deployable Model: APLUX-MODEL-FARM-LICENSE