The inception-resnet-v2 models re-trained from scratch via torch
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08 percent top-5 error on the test set of the ImageNet classification (CLS) challengeREAD FULL TEXT VIEW PDF
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A number of studies have shown that increasing the depth or width of
The inception-resnet-v2 models re-trained from scratch via torch
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task. Updated version here: https://github.com/Vooban/Hyperopt-Keras-CNN-CIFAR-100
Training the inception resnet v2 architecture in caffe
training wide residual networks in caffe
, their network “AlexNet” has been successfully applied to a larger variety of computer vision tasks, for example to object-detection, segmentation 
, human pose estimation, video classification , object tracking , and superresolution . These examples are but a few of all the applications to which deep convolutional networks have been very successfully applied ever since.
In this work we study the combination of the two most recent ideas: Residual connections introduced by He et al. in  and the latest revised version of the Inception architecture . In , it is argued that residual connections are of inherent importance for training very deep architectures. Since Inception networks tend to be very deep, it is natural to replace the filter concatenation stage of the Inception architecture with residual connections. This would allow Inception to reap all the benefits of the residual approach while retaining its computational efficiency.
Besides a straightforward integration, we have also studied whether Inception itself can be made more efficient by making it deeper and wider. For that purpose, we designed a new version named Inception-v4 which has a more uniform simplified architecture and more inception modules than Inception-v3. Historically, Inception-v3 had inherited a lot of the baggage of the earlier incarnations. The technical constraints chiefly came from the need for partitioning the model for distributed training using DistBelief
. Now, after migrating our training setup to TensorFlow these constraints have been lifted, which allowed us to simplify the architecture significantly. The details of that simplified architecture are described in Section 3.
In this report, we will compare the two pure Inception variants, Inception-v3 and v4, with similarly expensive hybrid Inception-ResNet versions. Admittedly, those models were picked in a somewhat ad hoc manner with the main constraint being that the parameters and computational complexity of the models should be somewhat similar to the cost of the non-residual models. In fact we have tested bigger and wider Inception-ResNet variants and they performed very similarly on the ImageNet classification challenge  dataset.
The last experiment reported here is an evaluation of an ensemble of all the best performing models presented here. As it was apparent that both Inception-v4 and Inception-ResNet-v2 performed similarly well, exceeding state-of-the art single frame performance on the ImageNet validation dataset, we wanted to see how a combination of those pushes the state of the art on this well studied dataset. Surprisingly, we found that gains on the single-frame performance do not translate into similarly large gains on ensembled performance. Nonetheless, it still allows us to report 3.1% top-5 error on the validation set with four models ensembled setting a new state of the art, to our best knowledge.
In the last section, we study some of the classification failures and conclude that the ensemble still has not reached the label noise of the annotations on this dataset and there is still room for improvement for the predictions.
Convolutional networks have become popular in large scale image recognition tasks after Krizhevsky et al. . Some of the next important milestones were Network-in-network  by Lin et al., VGGNet  by Simonyan et al. and GoogLeNet (Inception-v1)  by Szegedy et al.
Residual connection were introduced by He et al. in  in which they give convincing theoretical and practical evidence for the advantages of utilizing additive merging of signals both for image recognition, and especially for object detection. The authors argue that residual connections are inherently necessary for training very deep convolutional models. Our findings do not seem to support this view, at least for image recognition. However it might require more measurement points with deeper architectures to understand the true extent of beneficial aspects offered by residual connections. In the experimental section we demonstrate that it is not very difficult to train competitive very deep networks without utilizing residual connections. However the use of residual connections seems to improve the training speed greatly, which is alone a great argument for their use.
The Inception deep convolutional architecture was introduced in 
and was called GoogLeNet or Inception-v1 in our exposition. Later the Inception architecture was refined in various ways, first by the introduction of batch normalization (Inception-v2) by Ioffe et al. Later the architecture was improved by additional factorization ideas in the third iteration  which will be referred to as Inception-v3 in this report.
Our older Inception models used to be trained in a partitioned manner, where each replica was partitioned into a multiple sub-networks in order to be able to fit the whole model in memory. However, the Inception architecture is highly tunable, meaning that there are a lot of possible changes to the number of filters in the various layers that do not affect the quality of the fully trained network. In order to optimize the training speed, we used to tune the layer sizes carefully in order to balance the computation between the various model sub-networks. In contrast, with the introduction of TensorFlow our most recent models can be trained without partitioning the replicas. This is enabled in part by recent optimizations of memory used by backpropagation, achieved by carefully considering what tensors are needed for gradient computation and structuring the computation to reduce the number of such tensors. Historically, we have been relatively conservative about changing the architectural choices and restricted our experiments to varying isolated network components while keeping the rest of the network stable. Not simplifying earlier choices resulted in networks that looked more complicated that they needed to be. In our newer experiments, for Inception-v4 we decided to shed this unnecessary baggage and made uniform choices for the Inception blocks for each grid size. Plase refer to Figure9 for the large scale structure of the Inception-v4 network and Figures 3, 4, 5, 6, 7 and 8
for the detailed structure of its components. All the convolutions not marked with “V” in the figures are same-padded meaning that their output grid matches the size of their input. Convolutions marked with “V” are valid padded, meaning that input patch of each unit is fully contained in the previous layer and the grid size of the output activation map is reduced accordingly.
For the residual versions of the Inception networks, we use cheaper Inception blocks than the original Inception. Each Inception block is followed by filter-expansion layer ( convolution without activation) which is used for scaling up the dimensionality of the filter bank before the addition to match the depth of the input. This is needed to compensate for the dimensionality reduction induced by the Inception block.
We tried several versions of the residual version of Inception. Only two of them are detailed here. The first one “Inception-ResNet-v1” roughly the computational cost of Inception-v3, while “Inception-ResNet-v2” matches the raw cost of the newly introduced Inception-v4 network. See Figure 15
for the large scale structure of both varianets. (However, the step time of Inception-v4 proved to be significantly slower in practice, probably due to the larger number of layers.)
Another small technical difference between our residual and non-residual Inception variants is that in the case of Inception-ResNet, we used batch-normalization only on top of the traditional layers, but not on top of the summations. It is reasonable to expect that a thorough use of batch-normalization should be advantageous, but we wanted to keep each model replica trainable on a single GPU. It turned out that the memory footprint of layers with large activation size was consuming disproportionate amount of GPU-memory. By omitting the batch-normalization on top of those layers, we were able to increase the overall number of Inception blocks substantially. We hope that with better utilization of computing resources, making this trade-off will become unecessary.
Also we found that if the number of filters exceeded 1000, the residual variants started to exhibit instabilities and the network has just “died” early in the training, meaning that the last layer before the average pooling started to produce only zeros after a few tens of thousands of iterations. This could not be prevented, neither by lowering the learning rate, nor by adding an extra batch-normalization to this layer.
We found that scaling down the residuals before adding them to the previous layer activation seemed to stabilize the training. In general we picked some scaling factors between 0.1 and 0.3 to scale the residuals before their being added to the accumulated layer activations (cf. Figure 20).
A similar instability was observed by He et al. in  in the case of very deep residual networks and they suggested a two-phase training where the first “warm-up” phase is done with very low learning rate, followed by a second phase with high learning rata. We found that if the number of filters is very high, then even a very low (0.00001) learning rate is not sufficient to cope with the instabilities and the training with high learning rate had a chance to destroy its effects. We found it much more reliable to just scale the residuals.
Even where the scaling was not strictly necessary, it never seemed to harm the final accuracy, but it helped to stabilize the training.
We have trained our networks with stochastic gradient utilizing the TensorFlow 
distributed machine learning system usingreplicas running each on a NVidia Kepler GPU. Our earlier experiments used momentum  with a decay of
, while our best models were achieved using RMSProp with decay of and . We used a learning rate of
, decayed every two epochs using an exponential rate of. Model evaluations are performed using a running average of the parameters computed over time.
First we observe the top-1 and top-5 validation-error evolution of the four variants during training. After the experiment was conducted, we have found that our continuous evaluation was conducted on a subset of the validation set which omitted about 1700 blacklisted entities due to poor bounding boxes. It turned out that the omission should have been only performed for the CLSLOC benchmark, but yields somewhat incomparable (more optimistic) numbers when compared to other reports including some earlier reports by our team. The difference is about 0.3% for top- error and about 0.15% for the top- error. However, since the differences are consistent, we think the comparison between the curves is a fair one.
On the other hand, we have rerun our multi-crop and ensemble results on the complete validation set consisting of 50000 images. Also the final ensemble result was also performed on the test set and sent to the ILSVRC test server for validation to verify that our tuning did not result in an over-fitting. We would like to stress that this final validation was done only once and we have submitted our results only twice in the last year: once for the BN-Inception paper and later during the ILSVR-2015 CLSLOC competition, so we believe that the test set numbers constitute a true estimate of the generalization capabilities of our model.
Finally, we present some comparisons, between various versions of Inception and Inception-ResNet. The models Inception-v3 and Inception-v4 are deep convolutional networks not utilizing residual connections while Inception-ResNet-v1 and Inception-ResNet-v2 are Inception style networks that utilize residual connections instead of filter concatenation.
|Network||Top-1 Error||Top-5 Error|
|Network||Crops||Top-1 Error||Top-5 Error|
|Network||Crops||Top-1 Error||Top-5 Error|
|Network||Models||Top-1 Error||Top-5 Error|
|Inception-v4 + Inception-ResNet-v2||4||16.5%||3.1%|
Table 2 shows the single-model, single crop top-1 and top-5 error of the various architectures on the validation set.
Table 3 shows the performance of the various models with a small number of crops: 10 crops for ResNet as was reported in ), for the Inception variants, we have used the 12 crops evaluation as as described in .
Table 4 shows the single model performance of the various models using. For residual network the dense evaluation result is reported from . For the inception networks, the 144 crops strategy was used as described in .
We have presented three new network architectures in detail:
Inception-ResNet-v1: a hybrid Inception version that has a similar computational cost to Inception-v3 from .
Inception-ResNet-v2: a costlier hybrid Inception version with significantly improved recognition performance.
Inception-v4: a pure Inception variant without residual connections with roughly the same recognition performance as Inception-ResNet-v2.
We studied how the introduction of residual connections leads to dramatically improved training speed for the Inception architecture. Also our latest models (with and without residual connections) outperform all our previous networks, just by virtue of the increased model size.
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