(and their benchmarks and usecase)
| Name | Parameters (M) | Speed | Accuracy (ImageNet Top-1) |
|---|---|---|---|
| AlexNet | 60 | Slow | 56.5% |
| VGG-16 | 138 | Very Slow | ~71% (est.) |
| GoogLeNet (Inception V1) | 5 | Fast | ~69% (est.) |
| ResNet-50 | 26 | Moderate | ~80.3% |
| ResNet-101 | 44 | Moderate-Slow | ~80.3% |
| Inception-ResNet-V2 | 55 | Slow | 80.3% |
| MobileNetV3-Large | 5.5 | Very Fast | 75.27% |
| EfficientNetV2-S | 21.5 | Moderate-Fast | 84.23% |
| ConvNeXt-Tiny | 28.6 | Moderate | 82.52% |
| Name | Use Case |
|---|---|
| AlexNet | Historical, early object detection |
| VGG-16 | General high-accuracy detection |
| GoogLeNet (Inception V1) | Lightweight detection |
| ResNet-50 | High-accuracy general detection |
| ResNet-101 | High-accuracy complex datasets |
| Inception-ResNet-V2 | High-accuracy complex tasks |
| MobileNetV3-Large | Real-time detection on mobile/edge devices |
| EfficientNetV2-S | Versatile detection across domains |
| ConvNeXt-Tiny | Specialized high-accuracy tasks (e.g., infrared, remote sensing) |