Bootstrap Custom Image Classification using Transfer Learning by Danielle Dean and Wee Hyong at Strata Data Conference Singapore 2017
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Strata Data Conference Singapore 2017 https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/62981
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Bootstrap Custom Image Classification using Transfer Learning by Danielle Dean and Wee Hyong at Strata Data Conference Singapore 2017
- 1. Bootstrap Custom Image Classification using Transfer Learning Credits: Mark Hamilton, Ilia Karmanov, Anusua Trivedi, Vivek Gupta, Patrick Buehler, Alok Kirpal Danielle Dean PhD, Wee Hyong Tok PhD Principal Data Scientist Lead Cloud AI Microsoft @danielleodean | @weehyong Strata Singapore 2017
- 2. What are the common models? CNN RNN Convolutional Neural Network Recurrent Neural Network
- 3. Before 2017 2017 April ResNet-50 32 CPU 256 Nvidia P100 GPUs 1 hour ResNet-50 NVIDIA M40 GPU 14 days 1018 single precision operations Sept ResNet-50 1,600 CPUs 31 minutes Nov 15 minutes ResNet-50 1,024 P100 GPUs UC Berkeley, TACC, UC DavisFacebook Preferred Network ChainerMN
- 4. Overview Use Cases Image Classification using Transfer Learning How to jumpstart
- 6. Aerial Use Classification ESmart – Connected Drone and Power line inspection Jabil – Defect Inspection Use Cases Lung Cancer Detection
- 7. What is common in all these use cases?
- 8. What is common in all these use cases? Convolution Neural Network (CNN) + Transfer Learning
- 9. Clothing texture dataset: Applying transfer learning to accurately classify clothing texture Striped Argyle Dotted https://github.com/Azure/MachineLearningSamples- ImageClassificationUsingCNTK
- 10. Clothing texture dataset: Striped Argyle Dotted https://github.com/Azure/ImageSimilarityUsingCntk Applying transfer learning to accurately classify clothing texture
- 12. 14,197,122 images 21841 synsets Diverse images, Lots of labels!
- 20. 11x11 conv, 96, /4, pool/2 5x5 conv, 256, pool/2 3x3 conv, 384 3x3 conv, 384 3x3 conv, 256, pool/2 fc, 4096 fc, 4096 fc, 1000 AlexNet, 8 layers (ILSVRC 2012) 3x3 conv, 64 3x3 conv, 64, pool/2 3x3 conv, 128 3x3 conv, 128, pool/2 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256, pool/2 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512, pool/2 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512, pool/2 fc, 4096 fc, 4096 fc, 1000 VGG, 19 layers (ILSVRC 2014) input Conv 7x7+ 2(S) MaxPool 3x3+ 2(S) LocalRespNorm Conv 1x1+ 1(V) Conv 3x3+ 1(S) LocalRespNorm MaxPool 3x3+ 2(S) Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat MaxPool 3x3+ 2(S) Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) AveragePool 5x5+ 3(V) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) AveragePool 5x5+ 3(V) Dept hConcat MaxPool 3x3+ 2(S) Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat Conv Conv Conv Conv 1x1+ 1(S) 3x3+ 1(S) 5x5+ 1(S) 1x1+ 1(S) Conv Conv MaxPool 1x1+ 1(S) 1x1+ 1(S) 3x3+ 1(S) Dept hConcat AveragePool 7x7+ 1(V) FC Conv 1x1+ 1(S) FC FC Soft maxAct ivat ion soft max0 Conv 1x1+ 1(S) FC FC Soft maxAct ivat ion soft max1 Soft maxAct ivat ion soft max2 GoogleNet, 22 layers (ILSVRC 2014) ILSVRC (ImageNet Large Scale Visual Recognition Challenge)
- 21. ResNet, 152 layers 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x1 conv, 64 3x3 conv, 64 1x1 conv, 256 1x2 conv, 128, /2 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 1x1 conv, 128 3x3 conv, 128 1x1 conv, 512 7x7 conv, 64, /2, pool/2 Microsoft
- 22. Example – Visualizing the different layers Olah, et al., "Feature Visualization", Distill, 2017 https://distill.pub/2017/feature-visualization/ Another fun site: https://deepart.io/nips/submissions/random/ http://cs231n.stanford.edu/
- 23. Example – Visualizing the different layers Olah, et al., "Feature Visualization", Distill, 2017 https://distill.pub/2017/feature-visualization/ Another fun site: https://deepart.io/nips/submissions/random/ http://cs231n.stanford.edu/
- 26. Types of Transfer Learning Type How to Initialize Featurization Layers Output Layer Initialization How is Transfer Learning used? How to Train? Standard DNN Random Random None Train featurization and output jointly Headless DNN Learn using another task Separate ML algorithm Use the features learned on a related task Use the features to train a separate classifier Fine Tune DNN Learn using another task Random Use and fine tune features learned on a related task Train featurization and output jointly with a small learning rate Multi-Task DNN Random Random Learned features need to solve many related tasks Share a featurization network across both tasks. Train all networks jointly with a loss function (sum of individual task loss function)
- 27. Pre-Built CNN from General Task on Millions of Images Output Layer Stripped cat? YES dog? NO car? NO Classi fier e.g. SVM dotted? Complex Objects & Scenes (people, animals, cars, beach scene, etc.) Low-Level Features (lines, edges, color fields, etc.) High-Level Features (corners, contours, simple shapes) Object Parts (wheels, faces, windows, etc.) Outputs of penultimate layer of ImageNet Trained CNN provide excellent general purpose image features
- 28. Pre-Built CNN from General Task on Millions of Images Output Layer Stripped Using a pre-trained DNN, an accurate model can be achieved with thousands (or less) of labeled examples instead of millions cat? YES dog? NO car? NO dotted? Train one or more layers in new network
- 29. DNN featurization Input Image Size: 224x224 pixels Area Under Curve: 0.59 Classification Accuracy: 69.0% Fine-tuning (full CNN) Input Image Size: 224x224 pixels Area Under Curve: 0.76 Classification Accuracy: 77.4% Fine-tuning (full CNN) Input Image Size: 896x886 pixels Area Under Curve: 0.83 Classification Accuracy: 88.2%
- 30. How do you get started with transfer learning?
- 31. Pre-trained Models http://bit.ly/2jf97NE http://bit.ly/2zNiN8B http://bit.ly/1KlVMf0
- 33. Summary Use Cases Image Classification using Transfer Learning How to jumpstart