MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an inverted residual block (from MobileNetV2).
Source: MnasNet: Platform-Aware Neural Architecture Search for MobilePaper | Code | Results | Date | Stars |
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Task | Papers | Share |
---|---|---|
Image Classification | 5 | 20.00% |
Object Detection | 5 | 20.00% |
Quantization | 2 | 8.00% |
Model Compression | 2 | 8.00% |
General Classification | 2 | 8.00% |
BIG-bench Machine Learning | 1 | 4.00% |
Classification | 1 | 4.00% |
Scene Classification | 1 | 4.00% |
Scene Recognition | 1 | 4.00% |