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GoogLeNet Architecture Overview
geeksforgeeks.org/machine-learning/understanding-googlenet-model-cnn-architecture/Yapay zekadan makale özeti
- Core Features
- GoogLeNet is a deep CNN architecture for efficient image classification
- Uses Inception module with parallel 1x1, 3x3, 5x5 convolutions
- Contains 22 layers excluding pooling layers
- Accepts 224x224 RGB images as input
- Key Innovations
- 1x1 convolutions significantly reduce computation without compromising performance
- Global Average Pooling replaces fully connected layers with 1x1 convolutions
- Auxiliary classifiers address vanishing gradient problem during training
- Final layers use global average pooling for feature reduction
- Performance
- Won ILSVRC 2014 in both classification and detection tasks
- Achieved 6.67% top-5 error rate in image classification
- Six-model ensemble reached 43.9% mAP on ImageNet detection