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1/10 Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion Image classification using deep learning on a medium dataset made of cook recipes Remi Cadene under the direction Nicolas Thome and Matthieu Cord University Pierre and Marie Curie [email protected] November 25, 2015
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Image classification using deep learning on amedium dataset made of cook recipes
Remi Cadeneunder the direction Nicolas Thome and Matthieu Cord
University Pierre and Marie Curie
November 25, 2015
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Table of contents
1 Introduction
2 Overfeat, a deep convolutional network
3 Transfer Learning Experiments on UPMC Food-101
4 Conclusion
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Context
Since 2012, Deep ConvNet : AlexNet, Overfeat, Very Deep,GoogLeNetBenchmark dataset : ImageNet made of 1000 classes, 1.6 MimagesANR project (visiir), build a classifier from the dataset UPMCFood-101 to (90,840 images) classify photos of meals and togive the associated recipes
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
UPMC Food-101
Figure: Category examples of UPMC Food-101 dataset we used in ourstudy.
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Overfeat, a deep convolutional network
Figure: Overfeat accurate (144 Millions parameters)
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Transfer Learning Experiments on UPMC Food-101
1 Features ExtractionMethod : 80% train, 20% test, 5 foldsNetwork : Overfeat without FC layers + SVMResult : 31% of correct classification top 1Training time : 48 hours (5 folds)
2 Fine TuningMethod : 80% train, 20% test, 1 foldNetwork : Overfeat with clean FC layersResult : 44.6% of correct classification top 1Training time : 8 hours (7 epochs)
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Loss Fine Tuning
Figure: Loss processed during training time for each images on severalepochs of Overfeat fine tuned without data augmentation.
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Other Experiments
3 From ScratchMethod : 80% train, 20% test, 1 foldNetwork : Overfeat from scratchResult : 34.9% of correct classification top 1Training time : 60 hours (55 epochs)
4 Data AugmentationMethod : 80% train * 10, 20% test, 1 foldNetwork : Overfeat with clean FC layersResult : 49.5% of correct classification top 1Training time : 60 hours (4 epochs)
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Loss Fine Tuning with Data Augmentation
Figure: Loss processed during training time for each images of Overfeatfine tuned with data augmentation.
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Introduction Overfeat, a deep convolutional network Transfer Learning Experiments on UPMC Food-101 Conclusion
Conclusion
Pos Type Perf Test Training Time1 Fine Tuned (data*10) 49.5% 60 hours (4 epochs)2 Fine Tuned 44.6% 8 hours (7 epochs)3 From Scratch 34.9% 60 hours (55 epochs)4 Features Extract. (+SVM) 31% 48 hours (5 folds)
1 NotesFine Tuning is possible on a medium dataset10 ∗ training timesimple data >> training timedata∗10
10 ∗ epoch timesimple data < epoch timedata∗102 Future works
Transfer learning with more efficient networks : VeryDeep,GoogLeNetBetter hyperparameters optimization, batch normalizationlayers, implementing the Spatial Transformer module(DeepMind)
