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

Benchmark Model Rank Results
fine-grained-image-classification-on-fgvcAssemble-ResNet-FGVC-50#28Accuracy: 92.4
fine-grained-image-classification-on-food-101Assemble-ResNet-FGVC-50#7Accuracy: 92.5Top 1 Accuracy: 92.47
fine-grained-image-classification-on-oxfordAssemble-ResNet#8Accuracy: 98.9%
fine-grained-image-classification-on-oxford-2Assemble-ResNet-FGVC-50#6Accuracy: 94.3%Top-1 Error Rate: 5.7
fine-grained-image-classification-on-stanfordAssemble-ResNet-FGVC-50#33Accuracy: 94.4%
image-classification-on-imagenetAssemble-ResNet152#325Top 1 Accuracy: 84.2%GFLOPs: 15.8
image-classification-on-imagenet-realAssemble-ResNet152#27Accuracy: 88.65%
image-classification-on-imagenet-realAssemble ResNet-50#31Accuracy: 87.82%