Obscenity Detection in Images
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These slides discuss some milestone results in image classification using Deep Convolutional neural network and talks about our results on Obscenity detection in images by using Deep Convolutional neural network and transfer learning on ImageNet models.
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Obscenity Detection in Images
- 2. Anil Kumar Gupta Senior Software Engineer LinkedIn Image Classification
- 3. 3 Outline Why Image Classification? Classical Method of Image classification Convolution in Images Deep convolutional neural networks Success of DCNN on MNIST ImageNet Challenge Success of DCNN on ImageNet What DCNN learns DCNN on limited dataset Caffe Introduction Result of our dataset References and Resources
- 4. 4 Why Image Classification Introduced Group posts with Images Solutions: a) Let all Post with Images go through human review b) Small fraction of post go to human review. This fraction should include any obscene image posted
- 5. 5 Classical Method of Image classification Compute bag of features Bag of features are computed using different Image processing techniques Smoothing Derivatives of image Blob detection HOG SIFT …… …… Learn some ML model on these features
- 6. 6 Convolution in Images Compute bag of features Bag of features are computed using different Image processing techniques Smoothing Derivatives of image Blob detection HOG SIFT …… …… Learn some ML model on these features
- 8. 8
- 10. 10 Success of DCNN on MNIST (LeNet 1998) Source: Gradient Based Learning Applied to document Recognition
- 11. 11 Source: Gradient Based Learning Applied to document Recognition
- 12. 12 Ciresan et. al. net on MNIST (2010) Used lots of synthetic data to train bigger net Used Model averaging The right answer is almost always in the top 2 guesses. Source: Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs
- 13. 13 ImageNet Challenge (2012) The dataset has 1.2 million high-resolution training images. The classification task: Get the “correct” class in your top 5 bets. There are 1000 classes. Some of the best existing computer vision methods were tried on this dataset by leading computer vision groups from Oxford, INRIA, XRCE, …
- 14. 14 ImageNet Challenge Results (2012) University of Tokyo 26.1% Oxford University Computer Vision Group 26.9% INRIA (French national research institute in CS) + XRCE (Xerox Research Center Europe) 27.0% University of Amsterdam 29.5% University of Toronto (Alex Krizhevsky) 16.4 %
- 15. 15 ImageNet Challenge Results (2012) Why so many years for another successful result by DCNN ?
- 16. 16 Success of DCNN on ImageNet (AlexNet 2012) Source: ImageNet Classification with Deep Convolutional Neural Networks
- 17. 17
- 18. 18 AlexNet (2012) Use of GPUs to train the network Use of Data Augmentation Use of ReLU instead of Sigmoid Use of DropOut Layers
- 19. 19 ImageNet Results 2010 - 2015
- 20. 20 What CNN learns Source: Rich feature hierarchies for accurate object detection and semantic segmentation
- 21. 21 DCNN on limited dataset CNN learns hierarchical features Goes from generic features to task specific features Can use generic feature learned on different task for your specific task How transferable are features in deep neural networks? Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning Rich feature hierarchies for accurate object detection and semantic segmentation
- 22. 22 Caffe A DNN library Let you define DNN architecture in plain text file Let you define parameters in plain text file Can train on GPU and CPU Can train on GPU and deploy in CPU
- 23. 23 Layer Setup: run once for initialization. Forward: make output given input. Backward: make gradient of output - w.r.t. bottom - w.r.t. parameters (if needed) Reshape: set dimensions.
- 25. 25 Blob Blobs are 4-D arrays for storing and communicating information. hold data, derivatives, and parameters lazily allocate memory shuttle between CPU and GPU top blob bottom blob
- 26. 26 Our results from Transfer learning Training image: 5872 Good: 3075 Bad: 2797 Validation images: 1679 Good: 880 Bad: 799 Test images: 831 Good: 436 Bad: 395 Precision ~ 95% Recall ~ 95%
- 27. 27 In Production Bad images is ~ 1 in 1000 5 - 7 % send for human review Have not received any bad image post via user flagging Latency ~ 600 ms per image 5-6 QPS on single machine
- 28. 28 Reference & Resources Gradient Based Learning Applied to document Recognition Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs ImageNet Classification with Deep Convolutional Neural Networks Rich feature hierarchies for accurate object detection and semantic segmentation How transferable are features in deep neural networks? Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks Monza: Image Classification of Vehicle Make and Model Using Convolutional Neural Networks and Transfer Learning Dropout: A Simple Way to Prevent Neural Networks from Overfitting Mathematical Intuition for Performance of Rectified Linear Unit in Deep Neural Networks Convolutional Neural Network for Visual Recognition Neural Networks for Machine Learning Practical Recommendation for Train DNN
- 29. ©2014 LinkedIn Corporation. All Rights Reserved.©2014 LinkedIn Corporation. All Rights Reserved.