metadata
license: apache-2.0
ResNet-50: Image Classification
ResNet is a network with a better effect on classification problems in the ImageNet competition.
It introduces the concept of residual learning, protects the integrity of information by adding direct channels, and solves problems such as information loss, gradient disappearance, and gradient explosion. The network is also trained. ResNet has different network layers, commonly used are 18-layer, 34-layer, 50-layer, 101-layer, 152-layer. The meaning of ResNet50 means that there are 50-layers in the network. It is currently more commonly used because it takes into account both speed and accuracy.
The model can be found here
| Device | SoC | Runtime | Model | Size (pixels) | Inference Time (ms) | Precision | Compute Unity | Model Download |
|---|---|---|---|---|---|---|---|---|
| AidBox QCS6490 | QCS6490 | QNN | ResNet-50 | 224 | 3.6 | INT8 | NPU | model download |
| AidBox QCS6490 | QCS6490 | QNN | ResNet-50 | 224 | 5.9 | INT16 | NPU | model download |
| AidBox QCS6490 | QCS6490 | SNPE | ResNet-50 | 224 | 3.4 | INT8 | NPU | model download |
| AidBox QCS6490 | QCS6490 | SNPE | ResNet-50 | 224 | 4.8 | INT16 | NPU | model download |
| AidBox GS865 | QCS8250 | SNPE | ResNet-50 | 224 | 21 | INT8 | NPU | model download |
| APLUX QCS8550 | QCS8550 | QNN | ResNet-50 | 224 | 2.5 | INT8 | NPU | model download |
| APLUX QCS8550 | QCS8550 | QNN | ResNet-50 | 224 | 3.5 | INT16 | NPU | model download |
| APLUX QCS8550 | QCS8550 | SNPE | ResNet-50 | 224 | 1 | INT8 | NPU | model download |
| APLUX QCS8550 | QCS8550 | SNPE | ResNet-50 | 224 | 1.4 | INT16 | NPU | model download |