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

17 Jan 2020arXiv:2001.06268archive 2025-07-28

Jungkyu Lee, Taeryun Won, Tae Kwan Lee, Hyemin Lee, Geonmo Gu, Kiho 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. In this study, we carry out extensive experiments to validate that carefully assembling these techniques and applying them to basic CNN models (e.g. ResNet and MobileNet) can improve the accuracy and robustness of the models while minimizing the loss of throughput. Our proposed assembled ResNet-50 shows improvements in top-1 accuracy from 76.3\% to 82.78\%, mCE from 76.0\% to 48.9\% and mFR from 57.7\% to 32.3\% on ILSVRC2012 validation set. With these improvements, inference throughput only decreases from 536 to 312. To verify the performance improvement in transfer learning, fine grained classification and image retrieval tasks were tested on several public datasets and showed that the improvement to backbone network performance boosted transfer learning performance significantly. Our approach achieved 1st place in the iFood Competition Fine-Grained Visual Recognition at CVPR 2019, and the source code and trained models are available at https://github.com/clovaai/assembled-cnn

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Code

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fixed_padding clovaai/assembled-cnn/nets/model_helper.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · 5756d6bfbcce2dfc · report
anti_aliased_downsample clovaai/assembled-cnn/nets/blocks.py official repository unverified Apache-2.0 (permissive) · 0970ec81afd374c8 · report
batch_norm clovaai/assembled-cnn/nets/model_helper.py official repository unverified Apache-2.0 (permissive) · aa9fde2ef364ebff · report
conv2d_fixed_padding clovaai/assembled-cnn/nets/model_helper.py official repository unverified Apache-2.0 (permissive) · c756a6986ed2d1b8 · report
generalized_mean_pooling clovaai/assembled-cnn/nets/blocks.py official repository unverified Apache-2.0 (permissive) · bc3aa92a65ffc867 · report
get_block_sizes clovaai/assembled-cnn/functions/model_fns.py official repository unverified Apache-2.0 (permissive) · 8ca9aa61ab881fc5 · report
get_sup_loss clovaai/assembled-cnn/losses/cls_losses.py official repository unverified Apache-2.0 (permissive) · 58ddea37201045ea · report
get_train_op clovaai/assembled-cnn/nets/optimizer_setting.py official repository unverified Apache-2.0 (permissive) · 8e9e9366b2d2231c · report
keep_prob_decay clovaai/assembled-cnn/functions/model_fns.py official repository unverified Apache-2.0 (permissive) · 3a511a9f3d42665c · report
learning_rate_with_decay clovaai/assembled-cnn/functions/model_fns.py official repository unverified Apache-2.0 (permissive) · d9ed2858fbdb4769 · report
sk_conv2d clovaai/assembled-cnn/nets/blocks.py official repository unverified Apache-2.0 (permissive) · 58b86e2110d9f973 · report

Tasks

Fine-Grained Image ClassificationFine-Grained Visual RecognitionGeneral ClassificationImage ClassificationImage RetrievalRetrievalTransfer Learningimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Fine-Grained Image Classification FGVC Aircraft Assemble-ResNet-FGVC-50 Accuracy 92.4 #41 of 57 Archive leaderboard report
Fine-Grained Image Classification Food-101 Assemble-ResNet-FGVC-50 Accuracy 92.5 #9 of 15 Archive leaderboard report
Fine-Grained Image Classification Food-101 Assemble-ResNet-FGVC-50 Top 1 Accuracy 92.47 #9 of 15 Archive leaderboard report
Fine-Grained Image Classification Oxford 102 Flowers Assemble-ResNet Accuracy 98.9% #9 of 25 Archive leaderboard report
Fine-Grained Image Classification Oxford-IIIT Pets Assemble-ResNet-FGVC-50 Accuracy 94.3% #6 of 19 Archive leaderboard report
Fine-Grained Image Classification Oxford-IIIT Pets Assemble-ResNet-FGVC-50 Top-1 Error Rate 5.7 #6 of 19 Archive leaderboard report
Fine-Grained Image Classification SOP Assemble-ResNet-FGVC-50 Recall@1 85.9 #1 of 1 Archive leaderboard report
Fine-Grained Image Classification Stanford Cars Assemble-ResNet-FGVC-50 Accuracy 94.4% #47 of 83 Archive leaderboard report
Image Classification ImageNet Assemble-ResNet152 GFLOPs 15.8 #332 of 1060 Archive leaderboard report
Image Classification ImageNet Assemble-ResNet152 Top 1 Accuracy 84.2% #332 of 1060 Archive leaderboard report
Image Classification ImageNet ReaL Assemble-ResNet152 Accuracy 88.65% #27 of 57 Archive leaderboard report
Image Classification ImageNet ReaL Assemble ResNet-50 Accuracy 87.82% #31 of 57 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAnti-Alias DownsamplingAssemble-ResNetAutoAugmentAverage PoolingBatch NormalizationBig-Little ModuleBottleneck Residual BlockColorJitterConvolutionCosine AnnealingCutoutDense ConnectionsDepthwise ConvolutionDepthwise Separable ConvolutionDilated ConvolutionDropBlockGlobal Average PoolingKaiming InitializationLSTMLabel SmoothingLinear LayerLinear Warmup With Cosine AnnealingMax PoolingMixupMobileNetV1Pointwise ConvolutionRandom Horizontal FlipRandom Resized CropReLUResNet-DResidual BlockResidual ConnectionSGD with MomentumSelective KernelSelective Kernel ConvolutionSigmoid ActivationSoftmaxTanh ActivationWeight DecayXavier Initialization

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