Fixing the train-test resolution discrepancy: FixEfficientNet

18 Mar 2020  ·  Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Hervé Jégou ·

This paper provides an extensive analysis of the performance of the EfficientNet image classifiers with several recent training procedures, in particular one that corrects the discrepancy between train and test images. The resulting network, called FixEfficientNet, significantly outperforms the initial architecture with the same number of parameters. For instance, our FixEfficientNet-B0 trained without additional training data achieves 79.3% top-1 accuracy on ImageNet with 5.3M parameters. This is a +0.5% absolute improvement over the Noisy student EfficientNet-B0 trained with 300M unlabeled images. An EfficientNet-L2 pre-trained with weak supervision on 300M unlabeled images and further optimized with FixRes achieves 88.5% top-1 accuracy (top-5: 98.7%), which establishes the new state of the art for ImageNet with a single crop. These improvements are thoroughly evaluated with cleaner protocols than the one usually employed for Imagenet, and particular we show that our improvement remains in the experimental setting of ImageNet-v2, that is less prone to overfitting, and with ImageNet Real Labels. In both cases we also establish the new state of the art.

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Datasets


Results from the Paper


Ranked #7 on Image Classification on ImageNet ReaL (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Image Classification ImageNet FixEfficientNet-L2 Top 1 Accuracy 88.5% # 23
Top 5 Accuracy 98.7% # 3
Number of params 480M # 23
Hardware Burden None # 1
Operations per network pass None # 1
Image Classification ImageNet FixEfficientNet-B3 Top 1 Accuracy 85% # 135
Top 5 Accuracy 97.4% # 28
Number of params 12M # 282
Image Classification ImageNet FixEfficientNet-B8 Top 1 Accuracy 85.7% # 100
Top 5 Accuracy 97.6% # 20
Image Classification ImageNet FixEfficientNet-B4 Top 1 Accuracy 85.9% # 90
Top 5 Accuracy 97.7% # 17
Number of params 19M # 267
Image Classification ImageNet FixEfficientNet-B5 Top 1 Accuracy 86.4% # 69
Top 5 Accuracy 97.9% # 13
Number of params 30M # 205
Image Classification ImageNet FixEfficientNet-B6 Top 1 Accuracy 86.7% # 59
Top 5 Accuracy 98.0% # 11
Number of params 43M # 178
Image Classification ImageNet FixEfficientNet-B7 Top 1 Accuracy 87.1% # 48
Top 5 Accuracy 98.2% # 9
Number of params 66M # 126
Image Classification ImageNet FixEfficientNetB4 Top 1 Accuracy 84.0% # 175
Top 5 Accuracy 97.0% # 38
Number of params 19M # 267
Image Classification ImageNet FixEfficientNet-B0 Top 1 Accuracy 80.2% # 374
Top 5 Accuracy 95.4% # 97
Number of params 5.3M # 330
Image Classification ImageNet FixEfficientNet-B1 Top 1 Accuracy 82.6% # 259
Top 5 Accuracy 96.5% # 58
Number of params 7.8M # 304
Image Classification ImageNet FixEfficientNet-B2 Top 1 Accuracy 83.6% # 198
Top 5 Accuracy 96.9% # 43
Number of params 9.2M # 297
Image Classification ImageNet ReaL FixEfficientNet-L2 Accuracy 90.9% # 7
Params 480M # 44
Image Classification ImageNet ReaL FixEfficientNet-B8 Accuracy 90.0% # 19
Params 87M # 38

Methods