Papers › MultiGrain: a unified image embedding for classes and instances

MultiGrain: a unified image embedding for classes and instances

14 Feb 2019arXiv:1902.05509archive 2025-07-28

Maxim Berman, Hervé Jégou, Andrea Vedaldi, Iasonas Kokkinos, Matthijs Douze

MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval. It builds on a standard classification trunk. The top of the network produces an embedding containing coarse and fine-grained information, so that images can be recognized based on the object class, particular object, or if they are distorted copies. Our joint training is simple: we minimize a cross-entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation, with no need for additional labels. A key component of MultiGrain is a pooling layer that takes advantage of high-resolution images with a network trained at a lower resolution. When fed to a linear classifier, the learned embeddings provide state-of-the-art classification accuracy. For instance, we obtain 79.4% top-1 accuracy with a ResNet-50 learned on Imagenet, which is a +1.8% absolute improvement over the AutoAugment method. When compared with the cosine similarity, the same embeddings perform on par with the state-of-the-art for image retrieval at moderate resolutions.

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facebookresearch/multigrain officialmentioned in papermentioned on GitHubpytorchNOASSERTION report
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Tasks

ClassificationData AugmentationGeneral ClassificationImage ClassificationImage RetrievalObjectRetrievalimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet MultiGrain PNASNet (500px) Top 1 Accuracy 83.6% #407 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain PNASNet (450px) Top 1 Accuracy 83.2% #447 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain SENet154 (450px) Top 1 Accuracy 83.1% #462 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain SENet154 (400px) Top 1 Accuracy 83.0% #473 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain SENet154 (500px) Top 1 Accuracy 82.7% #506 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain PNASNet (400px) Top 1 Accuracy 82.6% #519 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain PNASNet (300px) Top 1 Accuracy 81.3% #648 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain R50-AA-500 Top 1 Accuracy 79.4% #755 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain R50-AA-224 Top 1 Accuracy 78.2% #845 of 1060 Archive leaderboard report
Image Classification ImageNet MultiGrain NASNet-A-Mobile (350px) Top 1 Accuracy 75.1% #958 of 1060 Archive leaderboard report
Image Retrieval INRIA Holidays MultiGrain R50 @ 800 Mean mAP 92.5% #1 of 2 Archive leaderboard report
Image Retrieval INRIA Holidays MultiGrain R50 @ 500 Mean mAP 91.8% #2 of 2 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

Introduced by this paper: MultiGrain

1x1 ConvolutionAutoAugmentAverage PoolingBatch NormalizationBottleneck Residual BlockColorJitterConvolutionCutoutGeneralized Mean PoolingGlobal Average PoolingKaiming InitializationLSTMMax PoolingMultiGrainPCA WhiteningRandom Horizontal FlipRandom Resized CropReLUResidual BlockResidual ConnectionSGDSigmoid ActivationTanh Activation

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