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EE-559 Deep learning 7. Networks for computer vision Fran cois Fleuret https://fleuret.org/dlc/ [version of: June 8, 2018] COLE POLYTECHNIQUE FDRALE DE LAUSANNE Tasks and data-sets Fran cois Fleuret EE-559 Deep learning

  1. The most standard networks for image classification are the LeNet family (leCun et al., 1998), and its modern extensions, among which AlexNet (Krizhevsky et al., 2012) and VGGNet (Simonyan and Zisserman, 2014). They share a common structure of several convolutional layers seen as a feature extractor, followed by fully connected layers seen as a classifier. The performance of AlexNet was a wake-up call for the computer vision community, as it vastly out-performed other methods in spite of its simplicity. Recent advances rely on moving from standard convolutional layers to local complex architectures to reduce the model size. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 18 / 89

  2. torchvision.models provides a collection of reference networks for computer vision, e.g. : import torchvision alexnet = torchvision .models.alexnet () Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 19 / 89

  3. torchvision.models provides a collection of reference networks for computer vision, e.g. : import torchvision alexnet = torchvision .models.alexnet () The trained models can be obtained by passing pretrained = True to the constructor(s). This may involve an heavy download given there size. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 19 / 89

  4. torchvision.models provides a collection of reference networks for computer vision, e.g. : import torchvision alexnet = torchvision .models.alexnet () The trained models can be obtained by passing pretrained = True to the constructor(s). This may involve an heavy download given there size. The networks from PyTorch listed in the coming slides may differ slightly � from the reference papers which introduced them historically. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 19 / 89

  5. LeNet5 (LeCun et al., 1989). 10 classes, input 1 × 28 × 28. (features): Sequential ( (0): Conv2d (1, 6, kernel_size =(5, 5), stride =(1, 1)) (1): ReLU (inplace) (2): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) (3): Conv2d (6, 16, kernel_size =(5, 5), stride =(1, 1)) (4): ReLU (inplace) (5): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) ) ( classifier ): Sequential ( (0): Linear (256 -> 120) (1): ReLU (inplace) (2): Linear (120 -> 84) (3): ReLU (inplace) (4): Linear (84 -> 10) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 20 / 89

  6. Alexnet (Krizhevsky et al., 2012). 1 , 000 classes, input 3 × 224 × 224. (features): Sequential ( (0): Conv2d (3, 64, kernel_size =(11 , 11) , stride =(4, 4), padding =(2, 2)) (1): ReLU (inplace) (2): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (3): Conv2d (64, 192, kernel_size =(5, 5), stride =(1, 1), padding =(2, 2)) (4): ReLU (inplace) (5): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (6): Conv2d (192 , 384, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (7): ReLU (inplace) (8): Conv2d (384 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (9): ReLU (inplace) (10): Conv2d (256 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (11): ReLU (inplace) (12): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) ) ( classifier ): Sequential ( (0): Dropout (p = 0.5) (1): Linear (9216 -> 4096) (2): ReLU (inplace) (3): Dropout (p = 0.5) (4): Linear (4096 -> 4096) (5): ReLU (inplace) (6): Linear (4096 -> 1000) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 21 / 89

  7. Krizhevsky et al. used data augmentation during training to reduce over-fitting. They generated 2 , 048 samples from every original training example through two classes of transformations: • crop a 224 × 224 image at a random position in the original 256 × 256, and randomly reflect it horizontally, • apply a color transformation using a PCA model of the color distribution. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 22 / 89

  8. Krizhevsky et al. used data augmentation during training to reduce over-fitting. They generated 2 , 048 samples from every original training example through two classes of transformations: • crop a 224 × 224 image at a random position in the original 256 × 256, and randomly reflect it horizontally, • apply a color transformation using a PCA model of the color distribution. During test the prediction is averaged over five random crops and their horizontal reflections. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 22 / 89

  9. VGGNet19 (Simonyan and Zisserman, 2014). 1 , 000 classes, input 3 × 224 × 224. 16 convolutional layers + 3 fully connected layers. (features): Sequential ( (0): Conv2d (3, 64, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (1): ReLU (inplace) (2): Conv2d (64, 64, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (3): ReLU (inplace) (4): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) (5): Conv2d (64, 128, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (6): ReLU (inplace) (7): Conv2d (128 , 128, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (8): ReLU (inplace) (9): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) (10): Conv2d (128 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (11): ReLU (inplace) (12): Conv2d (256 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (13): ReLU (inplace) (14): Conv2d (256 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (15): ReLU (inplace) (16): Conv2d (256 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (17): ReLU (inplace) (18): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) (19): Conv2d (256 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (20): ReLU (inplace) (21): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (22): ReLU (inplace) (23): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (24): ReLU (inplace) (25): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (26): ReLU (inplace) (27): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) (28): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (29): ReLU (inplace) (30): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (31): ReLU (inplace) (32): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (33): ReLU (inplace) (34): Conv2d (512 , 512, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (35): ReLU (inplace) (36): MaxPool2d (size =(2, 2), stride =(2, 2), dilation =(1, 1)) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 23 / 89

  10. VGGNet19 (cont.) ( classifier): Sequential ( (0): Linear (25088 -> 4096) (1): ReLU (inplace) (2): Dropout (p = 0.5) (3): Linear (4096 -> 4096) (4): ReLU (inplace) (5): Dropout (p = 0.5) (6): Linear (4096 -> 1000) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 24 / 89

  11. We can illustrate the convenience of these pre-trained models on a simple image-classification problem. To be sure this picture did not appear in the training data, it was not taken from the web. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 25 / 89

  12. import PIL , torch , torchvision # Load and normalize the image img = torchvision .transforms .ToTensor ()(PIL.Image.open(’blacklab.jpg ’)) img = img.view(1, img.size (0) , img.size (1) , img.size (2)) img = 0.5 + 0.5 * (img - img.mean ()) / img.std () Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 26 / 89

  13. import PIL , torch , torchvision # Load and normalize the image img = torchvision .transforms .ToTensor ()(PIL.Image.open(’blacklab.jpg ’)) img = img.view(1, img.size (0) , img.size (1) , img.size (2)) img = 0.5 + 0.5 * (img - img.mean ()) / img.std () # Load an already trained network and compute its prediction alexnet = torchvision .models.alexnet(pretrained = True) alexnet.eval () output = alexnet(Variable(img)) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 26 / 89

  14. import PIL , torch , torchvision # Load and normalize the image img = torchvision .transforms .ToTensor ()(PIL.Image.open(’blacklab.jpg ’)) img = img.view(1, img.size (0) , img.size (1) , img.size (2)) img = 0.5 + 0.5 * (img - img.mean ()) / img.std () # Load an already trained network and compute its prediction alexnet = torchvision .models.alexnet(pretrained = True) alexnet.eval () output = alexnet(Variable(img)) # Prints the classes scores , indexes = output.data.view (-1).sort(descending = True) class_names = eval(open(’ imagenet1000_clsid_to_human .txt ’, ’r’).read ()) for k in range (15): print ( ’#{:d} ({:.02f}) {:s}’. format(k, scores[k], class_names [indexes[k]])) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 26 / 89

  15. #1 (12.26) Weimaraner #2 (10.95) Chesapeake Bay retriever #3 (10.87) Labrador retriever #4 (10.10) Staffordshire bullterrier , Staffordshire bull terrier #5 (9.55) flat -coated retriever #6 (9.40) Italian greyhound #7 (9.31) American Staffordshire terrier , Staffordshire terrier , American pit bull terrier , pit bull terrier #8 (9.12) Great Dane #9 (8.94) German short -haired pointer #10 (8.53) Doberman , Doberman pinscher #11 (8.35) Rottweiler #12 (8.25) kelpie #13 (8.24) barrow , garden cart , lawn cart , wheelbarrow #14 (8.12) bucket , pail #15 (8.07) soccer ball Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 27 / 89

  16. #1 (12.26) Weimaraner #2 (10.95) Chesapeake Bay retriever #3 (10.87) Labrador retriever #4 (10.10) Staffordshire bullterrier , Staffordshire bull terrier #5 (9.55) flat -coated retriever #6 (9.40) Italian greyhound #7 (9.31) American Staffordshire terrier , Staffordshire terrier , American pit bull terrier , pit bull terrier #8 (9.12) Great Dane #9 (8.94) German short -haired pointer #10 (8.53) Doberman , Doberman pinscher #11 (8.35) Rottweiler #12 (8.25) kelpie #13 (8.24) barrow , garden cart , lawn cart , wheelbarrow #14 (8.12) bucket , pail #15 (8.07) soccer ball Weimaraner Chesapeake Bay retriever Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 27 / 89

  17. Fully convolutional networks Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 28 / 89

  18. In many applications, standard convolutional networks are made fully convolutional by converting their fully connected layers to convolutional ones. C W H x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 29 / 89

  19. In many applications, standard convolutional networks are made fully convolutional by converting their fully connected layers to convolutional ones. C W Reshape HWC H x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 29 / 89

  20. In many applications, standard convolutional networks are made fully convolutional by converting their fully connected layers to convolutional ones. C HWC W Reshape H x ( l +1) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 29 / 89

  21. In many applications, standard convolutional networks are made fully convolutional by converting their fully connected layers to convolutional ones. C C W W ⊛ H H x ( l +1) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 29 / 89

  22. In particular multiple 1 × 1 convolutions can be interpreted as computing a fully-connected layer at every location of an activation map. w ( l +1) Reshape x ( l +1) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 30 / 89

  23. In particular multiple 1 × 1 convolutions can be interpreted as computing a fully-connected layer at every location of an activation map. w ( l +1) w ( l +2) Reshape x ( l +2) x ( l +1) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 30 / 89

  24. In particular multiple 1 × 1 convolutions can be interpreted as computing a fully-connected layer at every location of an activation map. w ( l +1) ⊛ x ( l +1) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 30 / 89

  25. In particular multiple 1 × 1 convolutions can be interpreted as computing a fully-connected layer at every location of an activation map. w ( l +1) ⊛ ⊛ w ( l +2) x ( l +1) x ( l +2) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 30 / 89

  26. This “convolutionization” does not change anything if the input size is such that the output has a single spatial cell, but it fully re-uses computation to get a prediction at multiple locations when the input is larger. w ( l +1) ⊛ ⊛ w ( l +2) x ( l +1) x ( l +2) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 31 / 89

  27. This “convolutionization” does not change anything if the input size is such that the output has a single spatial cell, but it fully re-uses computation to get a prediction at multiple locations when the input is larger. w ( l +1) ⊛ ⊛ w ( l +2) x ( l +1) x ( l +2) x ( l +1) x ( l +2) x ( l ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 31 / 89

  28. We can write a routine that transforms a series of layers from a standard convnets to make it fully convolutional: def convolutionize (layers , input_size ): l = [] x = Variable(torch.zeros(torch.Size ((1, ) + input_size))) for m in layers: if isinstance(m, nn.Linear): n = nn.Conv2d( in_channels = x.size (1) , out_channels = m.weight.size (0) , kernel_size = (x.size (2) , x.size (3))) n.weight.data.view (-1).copy_(m.weight.data.view (-1)) n.bias.data.view (-1).copy_(m.bias.data.view (-1)) m = n l.append(m) x = m(x) return l model = torchvision .models.alexnet(pretrained = True) model = nn. Sequential ( * convolutionize (list(model.features) + list(model. classifier ), (3, 224, 224)) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 32 / 89

  29. We can write a routine that transforms a series of layers from a standard convnets to make it fully convolutional: def convolutionize (layers , input_size ): l = [] x = Variable(torch.zeros(torch.Size ((1, ) + input_size))) for m in layers: if isinstance(m, nn.Linear): n = nn.Conv2d( in_channels = x.size (1) , out_channels = m.weight.size (0) , kernel_size = (x.size (2) , x.size (3))) n.weight.data.view (-1).copy_(m.weight.data.view (-1)) n.bias.data.view (-1).copy_(m.bias.data.view (-1)) m = n l.append(m) x = m(x) return l model = torchvision .models.alexnet(pretrained = True) model = nn. Sequential ( * convolutionize (list(model.features) + list(model. classifier ), (3, 224, 224)) ) This function makes the [strong and disputable] assumption that only � nn.Linear has to be converted. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 32 / 89

  30. Original Alexnet AlexNet ( (features): Sequential ( (0): Conv2d (3, 64, kernel_size =(11 , 11) , stride =(4, 4), padding =(2, 2)) (1): ReLU (inplace) (2): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (3): Conv2d (64, 192, kernel_size =(5, 5), stride =(1, 1), padding =(2, 2)) (4): ReLU (inplace) (5): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (6): Conv2d (192 , 384, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (7): ReLU (inplace) (8): Conv2d (384 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (9): ReLU (inplace) (10): Conv2d (256 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (11): ReLU (inplace) (12): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) ) ( classifier): Sequential ( (0): Dropout (p = 0.5) (1): Linear (9216 -> 4096) (2): ReLU (inplace) (3): Dropout (p = 0.5) (4): Linear (4096 -> 4096) (5): ReLU (inplace) (6): Linear (4096 -> 1000) ) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 33 / 89

  31. Result of convolutionize Sequential ( (0): Conv2d (3, 64, kernel_size =(11 , 11) , stride =(4, 4), padding =(2, 2)) (1): ReLU (inplace) (2): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (3): Conv2d (64, 192, kernel_size =(5, 5), stride =(1, 1), padding =(2, 2)) (4): ReLU (inplace) (5): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (6): Conv2d (192 , 384, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (7): ReLU (inplace) (8): Conv2d (384 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (9): ReLU (inplace) (10): Conv2d (256 , 256, kernel_size =(3, 3), stride =(1, 1), padding =(1, 1)) (11): ReLU (inplace) (12): MaxPool2d (size =(3, 3), stride =(2, 2), dilation =(1, 1)) (13): Dropout (p = 0.5) (14): Conv2d (256 , 4096 , kernel_size =(6, 6), stride =(1, 1)) (15): ReLU (inplace) (16): Dropout (p = 0.5) (17): Conv2d (4096 , 4096 , kernel_size =(1, 1), stride =(1, 1)) (18): ReLU (inplace) (19): Conv2d (4096 , 1000 , kernel_size =(1, 1), stride =(1, 1)) ) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 34 / 89

  32. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d FC layers Max-pooling Conv layers Input image AlexNet random cropping Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  33. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d FC layers Max-pooling Conv layers Input image AlexNet random cropping Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  34. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d FC layers Max-pooling Conv layers Input image AlexNet random cropping Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  35. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d FC layers Max-pooling Conv layers Input image AlexNet random cropping Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  36. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d FC layers Max-pooling Conv layers Input image AlexNet random cropping Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  37. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d FC layers Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  38. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d 1000d FC layers FC layers Max-pooling Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  39. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d 1000d FC layers FC layers Max-pooling Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  40. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d 1000d FC layers FC layers Max-pooling Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  41. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d 1000d FC layers FC layers Max-pooling Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  42. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d 1000d FC layers FC layers Max-pooling Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  43. In their “overfeat” approach, Sermanet et al. (2013) combined this with a stride 1 final max-pooling to get multiple predictions. 1000d 1000d FC layers FC layers Max-pooling Max-pooling Conv layers Conv layers Input image Input image AlexNet random cropping Overfeat dense max-pooling Doing so, they could afford parsing the scene at 6 scales to improve invariance. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 35 / 89

  44. This “convolutionization” has a practical consequence, as we can now re-use classification networks for dense prediction without re-training. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 36 / 89

  45. This “convolutionization” has a practical consequence, as we can now re-use classification networks for dense prediction without re-training. Also, and maybe more importantly, it blurs the conceptual boundary between “features” and “classifier” and leads to an intuitive understanding of convnet activations as gradually transitioning from appearance to semantic. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 36 / 89

  46. In the case of a large output prediction map, a final prediction can be obtained by averaging the final output map channel-wise. If the last layer is linear, the averaging can be done first, as in the residual networks (He et al., 2015). Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 37 / 89

  47. Image classification, network in network Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 38 / 89

  48. Lin et al. (2013) re-interpreted a convolution filter as a one-layer perceptron, and extended it with an “MLP convolution” (aka “network in network”) to improve the capacity vs. parameter ratio. . . . . . . (Lin et al., 2013) As for the fully convolutional networks, such local MLPs can be implemented with 1 × 1 convolutions. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 39 / 89

  49. The same notion was generalized by Szegedy et al. (2015) for their GoogLeNet, through the use of module combining convolutions at multiple scales to let the optimal ones be picked during training. Filter Filter concatenation concatenation 3x3 convolutions 5x5 convolutions 1x1 convolutions 1x1 convolutions 3x3 convolutions 5x5 convolutions 3x3 max pooling 1x1 convolutions 1x1 convolutions 1x1 convolutions 3x3 max pooling Previous layer Previous layer (a) Inception module, na¨ ıve version (b) Inception module with dimension reductions (Szegedy et al., 2015) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 40 / 89

  50. Szegedy et al. (2015) also introduce the idea of auxiliary classifiers to help the propagation of the gradient in the early layers. This is motivated by the reasonable performance of shallow networks that indicates early layers already encode informative and invariant features. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 41 / 89

  51. 42 / 89 softmax2 SoftmaxActivation FC AveragePool 7x7+1(V) (Szegedy et al., 2015) DepthConcat It was later extended with techniques we are going to see in the next slides: Conv Conv Conv Conv The resulting GoogLeNet has 12 times less parameters than AlexNet and is 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) Conv Conv MaxPool 1x1+1(S) 1x1+1(S) 3x3+1(S) DepthConcat Conv Conv Conv Conv a la softmax1 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) Conv Conv MaxPool batch-normalization (Ioffe and Szegedy, 2015) and pass-through ` SoftmaxActivation 1x1+1(S) 1x1+1(S) 3x3+1(S) MaxPool FC 3x3+2(S) DepthConcat FC Conv Conv Conv Conv Conv 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) 1x1+1(S) Conv Conv MaxPool AveragePool 1x1+1(S) 1x1+1(S) 3x3+1(S) 5x5+3(V) DepthConcat EE-559 – Deep learning / 7. Networks for computer vision Conv Conv Conv Conv more accurate on ILSVRC14 (Szegedy et al., 2015). 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) Conv Conv MaxPool 1x1+1(S) 1x1+1(S) 3x3+1(S) DepthConcat softmax0 Conv Conv Conv Conv SoftmaxActivation 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) Conv Conv MaxPool FC 1x1+1(S) 1x1+1(S) 3x3+1(S) DepthConcat FC Conv Conv Conv Conv Conv 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) 1x1+1(S) Conv Conv MaxPool AveragePool 1x1+1(S) 1x1+1(S) 3x3+1(S) 5x5+3(V) DepthConcat Conv Conv Conv Conv 1x1+1(S) 3x3+1(S) 5x5+1(S) 1x1+1(S) Conv Conv MaxPool resnet (Szegedy et al., 2016). 1x1+1(S) 1x1+1(S) 3x3+1(S) MaxPool 3x3+2(S) DepthConcat 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) DepthConcat 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) MaxPool 3x3+2(S) LocalRespNorm Conv 3x3+1(S) Conv 1x1+1(V) LocalRespNorm MaxPool 3x3+2(S) Conv cois Fleuret 7x7+2(S) input Fran¸

  52. Image classification, residual networks Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 43 / 89

  53. We already saw the structure of the residual networks and how well they perform on CIFAR10 (He et al., 2015). The default residual block proposed by He et al. is of the form Conv Conv . . . . . . 3 × 3 BN ReLU 3 × 3 BN + ReLU 64 64 64 → 64 64 → 64 and as such requires 2 × (3 × 3 × 64 + 1) × 64 ≃ 73 k parameters. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 44 / 89

  54. To apply the same architecture to ImageNet, more channels are required, e.g. Conv Conv . . . . . . + 3 × 3 BN ReLU 3 × 3 BN ReLU 256 256 256 → 256 256 → 256 However, such a block requires 2 × (3 × 3 × 256 + 1) × 256 ≃ 1 . 2 m parameters. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 45 / 89

  55. To apply the same architecture to ImageNet, more channels are required, e.g. Conv Conv . . . . . . + 3 × 3 BN ReLU 3 × 3 BN ReLU 256 256 256 → 256 256 → 256 However, such a block requires 2 × (3 × 3 × 256 + 1) × 256 ≃ 1 . 2 m parameters. They mitigated that requirement with what they call a bottleneck block: Conv Conv Conv . . . . . . 1 × 1 BN ReLU 3 × 3 BN ReLU 1 × 1 BN + ReLU 256 256 256 → 64 64 → 64 64 → 256 256 × 64 + (3 × 3 × 64 + 1) × 64 + 64 × 256 ≃ 70 k parameters. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 45 / 89

  56. To apply the same architecture to ImageNet, more channels are required, e.g. Conv Conv . . . . . . + 3 × 3 BN ReLU 3 × 3 BN ReLU 256 256 256 → 256 256 → 256 However, such a block requires 2 × (3 × 3 × 256 + 1) × 256 ≃ 1 . 2 m parameters. They mitigated that requirement with what they call a bottleneck block: Conv Conv Conv . . . . . . 1 × 1 BN ReLU 3 × 3 BN ReLU 1 × 1 BN + ReLU 256 256 256 → 64 64 → 64 64 → 256 256 × 64 + (3 × 3 × 64 + 1) × 64 + 64 × 256 ≃ 70 k parameters. The encoding pushed between blocks is high-dimensional, but the “contextual reasoning” in convolutional layers is done on a simpler feature representation. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 45 / 89

  57. layer name output size 18-layer 34-layer 50-layer 101-layer 152-layer 112 × 112 7 × 7, 64, stride 2 conv1 3 × 3 max pool, stride 2       1 × 1, 64 1 × 1, 64 1 × 1, 64 conv2 x 56 × 56 � 3 × 3, 64 � � 3 × 3, 64 � × 2 × 3 3 × 3, 64  × 3 3 × 3, 64  × 3 3 × 3, 64  × 3 3 × 3, 64 3 × 3, 64    1 × 1, 256 1 × 1, 256 1 × 1, 256  1 × 1, 128   1 × 1, 128   1 × 1, 128  � � � � 3 × 3, 128 3 × 3, 128 28 × 28 × 2 × 4 3 × 3, 128  × 4 3 × 3, 128  × 4 3 × 3, 128  × 8 conv3 x    3 × 3, 128 3 × 3, 128 1 × 1, 512 1 × 1, 512 1 × 1, 512  1 × 1, 256   1 × 1, 256   1 × 1, 256  � 3 × 3, 256 � � 3 × 3, 256 � conv4 x 14 × 14 × 2 × 6 3 × 3, 256  × 6 3 × 3, 256  × 23 3 × 3, 256  × 36 3 × 3, 256 3 × 3, 256    1 × 1, 1024 1 × 1, 1024 1 × 1, 1024  1 × 1, 512   1 × 1, 512   1 × 1, 512  � 3 × 3, 512 � � 3 × 3, 512 � 7 × 7 × 2 × 3  × 3  × 3  × 3 conv5 x 3 × 3, 512 3 × 3, 512 3 × 3, 512 3 × 3, 512 3 × 3, 512    1 × 1, 2048 1 × 1, 2048 1 × 1, 2048 1 × 1 average pool, 1000-d fc, softmax FLOPs 1.8 × 10 9 3.6 × 10 9 3.8 × 10 9 7.6 × 10 9 11.3 × 10 9 Table 1. Architectures for ImageNet. Building blocks are shown in brackets (see also Fig. 5), with the numbers of blocks stacked. Down- sampling is performed by conv3 1, conv4 1, and conv5 1 with a stride of 2. (He et al., 2015) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 46 / 89

  58. top-5 err. ( test ) method VGG [41] (ILSVRC’14) 7.32 GoogLeNet [44] (ILSVRC’14) 6.66 VGG [41] (v5) 6.8 PReLU-net [13] 4.94 BN-inception [16] 4.82 ResNet (ILSVRC’15) 3.57 Table 5. Error rates (%) of ensembles . The top-5 error is on the test set of ImageNet and reported by the test server. (He et al., 2015) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 47 / 89

  59. This was extended to the ResNeXt architecture by Xie et al. (2016), with blocks with similar number of parameters, but split into 32 “aggregated” pathways. Conv Conv Conv 1 × 1 BN ReLU 3 × 3 BN ReLU 1 × 1 BN 256 → 4 4 → 4 4 → 256 . . . . . . . . . + ReLU 256 256 Conv Conv Conv 1 × 1 BN ReLU 3 × 3 BN ReLU 1 × 1 BN 256 → 4 4 → 4 4 → 256 Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 48 / 89

  60. This was extended to the ResNeXt architecture by Xie et al. (2016), with blocks with similar number of parameters, but split into 32 “aggregated” pathways. Conv Conv Conv 1 × 1 BN ReLU 3 × 3 BN ReLU 1 × 1 BN 256 → 4 4 → 4 4 → 256 . . . . . . . . . + ReLU 256 256 Conv Conv Conv 1 × 1 BN ReLU 3 × 3 BN ReLU 1 × 1 BN 256 → 4 4 → 4 4 → 256 When equalizing the number of parameters, this architecture performs better than a standard resnet. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 48 / 89

  61. Image classification, summary Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 49 / 89

  62. To summarize roughly the evolution of convnets for image classification: • standard ones are extensions of LeNet5, • everybody loves ReLU, • state-of-the-art networks have 100s of channels and 10s of layers, Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 50 / 89

  63. To summarize roughly the evolution of convnets for image classification: • standard ones are extensions of LeNet5, • everybody loves ReLU, • state-of-the-art networks have 100s of channels and 10s of layers, • they can (should?) be fully convolutional, Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 50 / 89

  64. To summarize roughly the evolution of convnets for image classification: • standard ones are extensions of LeNet5, • everybody loves ReLU, • state-of-the-art networks have 100s of channels and 10s of layers, • they can (should?) be fully convolutional, • pass-through connections allow deeper “residual” nets, • bottleneck local structures reduce the number of parameters, • aggregated pathways reduce the number of parameters. Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 50 / 89

  65. Image classification networks LeNet5 (LeCun et al., 1989) LSTM (Hochreiter and Schmidhuber, 1997) Bigger + GPU Deep hierarchical CNN (Ciresan et al., 2012) Bigger + ReLU + dropout Fully convolutional AlexNet (Krizhevsky et al., 2012) No recurrence MLPConv Bigger + Overfeat small filters (Sermanet et al., 2013) Net in Net (Lin et al., 2013) Inception VGG modules (Simonyan and Zisserman, 2014) Highway Net GoogLeNet (Srivastava et al., 2015) (Szegedy et al., 2015) Batch Normalization BN-Inception (Ioffe and Szegedy, 2015) No gating ResNet (He et al., 2015) Wider Dense Aggregated pass-through channels Wide ResNet DenseNet ResNeXt Inception-ResNet (Zagoruyko and Komodakis, 2016) (Huang et al., 2016) (Xie et al., 2016) (Szegedy et al., 2016) Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 51 / 89

  66. Object detection Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 52 / 89

  67. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  68. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  69. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  70. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  71. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  72. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. . . . Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  73. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  74. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  75. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  76. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  77. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

  78. The simplest strategy to move from image classification to object detection is to classify local regions, at multiple scales and locations. Parsing at fixed scale Final list of detections Fran¸ cois Fleuret EE-559 – Deep learning / 7. Networks for computer vision 53 / 89

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