SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size
Paper summary * The authors train a variant of AlexNet that has significantly fewer parameters than the original network, while keeping the network's accuracy stable. * Advantages of this: * More efficient distributed training, because less parameters have to be transferred. * More efficient transfer via the internet, because the model's file size is smaller. * Possibly less memory demand in production, because fewer parameters have to be kept in memory. ### How * They define a Fire Module. A Fire Module contains of: * Squeeze Module: A 1x1 convolution that reduces the number of channels (e.g. from 128x32x32 to 64x32x32). * Expand Module: A 1x1 convolution and a 3x3 convolution, both applied to the output of the Squeeze Module. Their results are concatenated. * Using many 1x1 convolutions is advantageous, because they need less parameters than 3x3s. * They use ReLUs, only convolutions (no fully connected layers) and Dropout (50%, before the last convolution). * They use late maxpooling. They argue that applying pooling late - rather than early - improves accuracy while not needing more parameters. * They try residual connections: * One network without any residual connections (performed the worst). * One network with residual connections based on identity functions, but only between layers of same dimensionality (performed the best). * One network with residual connections based on identity functions and other residual connections with 1x1 convs (where dimensionality changed) (performance between the other two). * They use pruning from Deep Compression to reduce the parameters further. Pruning simply collects the 50% of all parameters of a layer that have the lowest values and sets them to zero. That creates a sparse matrix. ### Results * 50x parameter reduction of AlexNet (1.2M parameters before pruning, 0.4M after pruning). * 510x file size reduction of AlexNet (from 250mb to 0.47mb) when combined with Deep Compression. * Top-1 accuracy remained stable. * Pruning apparently can be used safely, even after the network parameters have already been reduced significantly. * While pruning was generally safe, they found that two of their later layers reacted quite sensitive to it. Adding parameters to these (instead of removing them) actually significantly improved accuracy. * Generally they found 1x1 convs to react more sensitive to pruning than 3x3s. Therefore they focused pruning on 3x3 convs. * First pruning a network, then re-adding the pruned weights (initialized with 0s) and then retraining for some time significantly improved accuracy. * The network was rather resilient to significant channel reduction in the Squeeze Modules. Reducing to 25-50% of the original channels (e.g. 128x32x32 to 64x32x32) seemed to be a good choice. * The network was rather resilient to removing 3x3 convs and replacing them with 1x1 convs. A ratio of 2:1 to 1:1 (1x1 to 3x3) seemed to produce good results while mostly keeping the accuracy. * Adding some residual connections between the Fire Modules improved the accuracy. * Adding residual connections with identity functions *and also* residual connections with 1x1 convs (where dimensionality changed) improved the accuracy, but not as much as using *only* residual connections with identity functions (i.e. it's better to keep some modules without identity functions). -------------------- ### Rough chapter-wise notes * (1) Introduction and Motivation * Advantages from having less parameters: * More efficient distributed training, because less data (parameters) have to be transfered. * Less data to transfer to clients, e.g. when a model used by some app is updated. * FPGAs often have hardly any memory, i.e. a model has to be small to be executed. * Target here: Find a CNN architecture with less parameters than an existing one but comparable accuracy. * (2) Related Work * (2.1) Model Compression * SVD-method: Just apply SVD to the parameters of an existing model. * Network Pruning: Replace parameters below threshold with zeros (-> sparse matrix), then retrain a bit. * Add quantization and huffman encoding to network pruning = Deep Compression. * (2.2) CNN Microarchitecture * The term "CNN Microarchitecture" refers to the "organization and dimensions of the individual modules" (so an Inception module would have a complex CNN microarchitecture). * (2.3) CNN Macroarchitecture * CNN Macroarchitecture = "big picture" / organization of many modules in a network / general characteristics of the network, like depth * Adding connections between modules can help (e.g. residual networks) * (2.4) Neural Network Design Space Exploration * Approaches for Design Space Exporation (DSE): * Bayesian Optimization, Simulated Annealing, Randomized Search, Genetic Algorithms * (3) SqueezeNet: preserving accuracy with few parameters * (3.1) Architectural Design Strategies * A conv layer with N filters applied to CxHxW input (e.g. 3x128x128 for a possible first layer) with kernel size kHxkW (e.g. 3x3) has `N*C*kH*kW` parameters. * So one way to reduce the parameters is to decrease kH and kW, e.g. from 3x3 to 1x1 (reduces parameters by a factor of 9). * A second way is to reduce the number of channels (C), e.g. by using 1x1 convs before the 3x3 ones. * They think that accuracy can be improved by performing downsampling later in the network (if parameter count is kept constant). * (3.2) The Fire Module * The Fire Module has two components: * Squeeze Module: * One layer of 1x1 convs * Expand Module: * Concat the results of: * One layer of 1x1 convs * One layer of 3x3 convs * The Squeeze Module decreases the number of input channels significantly. * The Expand Module then increases the number of input channels again. * (3.3) The SqueezeNet architecture * One standalone conv, then several fire modules, then a standalone conv, then global average pooling, then softmax. * Three late max pooling laters. * Gradual increase of filter numbers. * (3.3.1) Other SqueezeNet details * ReLU activations * Dropout before the last conv layer. * No linear layers. * (4) Evaluation of SqueezeNet * Results of competing methods: * SVD: 5x compression, 56% top-1 accuracy * Pruning: 9x compression, 57.2% top-1 accuracy * Deep Compression: 35x compression, ~57% top-1 accuracy * SqueezeNet: 50x compression, ~57% top-1 accuracy * SqueezeNet combines low parameter counts with Deep Compression. * The accuracy does not go down because of that, i.e. apparently Deep Compression can even be applied to small models without giving up on performance. * (5) CNN Microarchitecture Design Space Exploration * (5.1) CNN Microarchitecture metaparameters * blabla we test various values for this and that parameter * (5.2) Squeeze Ratio * In a Fire Module there is first a Squeeze Module and then an Expand Module. The Squeeze Module decreases the number of input channels to which 1x1 and 3x3 both are applied (at the same time). * They analyzed how far you can go down with the Sqeeze Module by training multiple networks and calculating the top-5 accuracy for each of them. * The accuracy by Squeeze Ratio (percentage of input channels kept in 1x1 squeeze, i.e. 50% = reduced by half, e.g. from 128 to 64): * 12%: ~80% top-5 accuracy * 25%: ~82% top-5 accuracy * 50%: ~85% top-5 accuracy * 75%: ~86% top-5 accuracy * 100%: ~86% top-5 accuracy * (5.3) Trading off 1x1 and 3x3 filters * Similar to the Squeeze Ratio, they analyze the optimal ratio of 1x1 filters to 3x3 filters. * E.g. 50% would mean that half of all filters in each Fire Module are 1x1 filters. * Results: * 01%: ~76% top-5 accuracy * 12%: ~80% top-5 accuracy * 25%: ~82% top-5 accuracy * 50%: ~85% top-5 accuracy * 75%: ~85% top-5 accuracy * 99%: ~85% top-5 accuracy * (6) CNN Macroarchitecture Design Space Exploration * They compare the following networks: * (1) Without residual connections * (2) With residual connections between modules of same dimensionality * (3) With residual connections between all modules (except pooling layers) using 1x1 convs (instead of identity functions) where needed * Adding residual connections (2) improved top-1 accuracy from 57.5% to 60.4% without any new parameters. * Adding complex residual connections (3) worsed top-1 accuracy again to 58.8%, while adding new parameters. * (7) Model Compression Design Space Exploration * (7.1) Sensitivity Analysis: Where to Prune or Add parameters * They went through all layers (including each one in the Fire Modules). * In each layer they set the 50% smallest weights to zero (pruning) and measured the effect on the top-5 accuracy. * It turns out that doing that has basically no influence on the top-5 accuracy in most layers. * Two layers towards the end however had significant influence (accuracy went down by 5-10%). * Adding parameters to these layers improved top-1 accuracy from 57.5% to 59.5%. * Generally they found 1x1 layers to be more sensitive than 3x3 layers so they pruned them less aggressively. * (7.2) Improving Accuracy by Densifying Sparse Models * They found that first pruning a model and then retraining it again (initializing the pruned weights to 0) leads to higher accuracy. * They could improve top-1 accuracy by 4.3% in this way.
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size
Iandola, Forrest N. and Moskewicz, Matthew W. and Ashraf, Khalid and Han, Song and Dally, William J. and Keutzer, Kurt
arXiv e-Print archive - 2016 via Local Bibsonomy
Keywords: dblp

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