Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

17 Jan 2020Jungkyu LeeTaeryun WonTae Kwan LeeHyemin LeeGeonmo GuKiho Hong

Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs). However, attempts to combine existing techniques to create a practical model are still uncommon... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Fine-Grained Image Classification FGVC Aircraft Assemble-ResNet-FGVC-50 Accuracy 92.4% # 10
Fine-Grained Image Classification Food-101 Assemble-ResNet-FGVC-50 Top 1 Accuracy 92.5 # 1
Image Classification ImageNet Assemble-ResNet152 Top 1 Accuracy 84.2% # 23
Fine-Grained Image Classification Oxford 102 Flowers Assemble-ResNet-FGVC-50 Accuracy 98.9% # 4
Fine-Grained Image Classification Oxford-IIIT Pets Assemble-ResNet-FGVC-50 Top-1 Error Rate 5.7% # 4
Fine-Grained Image Classification Oxford-IIIT Pets Assemble-ResNet-FGVC-50 Accuracy 94.3% # 3
Fine-Grained Image Classification Stanford Cars Assemble-ResNet-FGVC-50 Accuracy 94.4% # 10