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
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)