Of course, you’ll have to have backed them up in the first place.
If you perform a factory reset on your phone or tablet, it’s pretty easy to restore the deleted photos through your Google account. Since July 2019, Google has separated its photo storage from Google Drive.
MY PHOTOS 2017 TO 2019 ANDROID
The overall parameter count could be significantly reduced by removing the SE blocks for the last stage with only a marginal loss of performance.Google Photos is integrated with the Android operating system, but it can also be used on iOS devices and desktop computers.This suggests that SE 5 2 and SE 5 3 are less important than previous blocks in providing recalibration to the network.Similar pattern is found in SE_5_3 with slight change in scale.
SE_5_2, it exhibits an interesting tendency towards a saturated state in which most of the activations are close to 1 and the remainder is close to 0.
As a result, representation learning benefits from the recalibration induced by SE blocks which adaptively facilitates feature extraction and specialisation to the extent that it is needed. SE_4_6 and SE_5_1, the value of each channel becomes much more class-specific as different classes exhibit different preferences to the discriminative value of features. SE_2_3, the importance of feature channels is likely to be shared by different classes in the early stages of the network. For the above 5 classes, fifty samples are drawn for each class from the validation set and compute the average activations for fifty uniformly sampled channels in the last SE block in each stage. The performance improvements are consistent through training across a range of different depths, suggesting that the improvements induced by SE blocks can be used in combination with increasing the depth of the base architecture.Īctivations induced by Excitation in the different modules of SE-ResNet-50 on ImageNet. SE-Inception-ResNet-v2 ( 4.79% top-5 error) outperforms the reimplemented Inception-ResNet-v2 ( 5.21% top-5 error) by 0.42% (a relative improvement of 8.1%). Similarly, SE-ResNeXt-50 has a top-5 error of 5.49% which is superior to both its direct counterpart ResNeXt-50 ( 5.90% top-5 error) as well as the deeper ResNeXt-101 ( 5.57% top-5 error), a model which has almost double the number of parameters and computational overhead. And SE-ResNet-101 (6.07% top-5 error) not only matches, but outperforms the deeper ResNet-152 network (6.34% top-5 error) by 0.27%. Remarkably, SE-ResNet-50 achieves a single-crop top-5 validation error of 6.62%, exceeding ResNet-50 (7.48%) by 0.86% and approaching the performance achieved by the much deeper ResNet-101 network (6.52% top-5 error) with only half of the computational overhead (3.87 GFLOPs vs. During testing, CPU inference time for each model for a 224 × 224 pixel input image: ResNet-50 takes 164 ms, compared to 167 ms for SE-ResNet-50. 
During training, with a mini-batch of 256 images, a single pass forwards and backwards through ResNet-50 takes 190 ms, compared to 209 ms for SE-ResNet-50 (both timings are performed on a server with 8 NVIDIA Titan X GPUs). For VGGNet, Batch Normalization layer is added after each convolution for easier training. SE Blocks are added to ResNet, ResNeXt, VGGNet, BN-Inception, and Inception-ResNet-v2. Single-Crop Error Rates (%) on ImageNet Validation Set
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