![]() ![]() ![]() While admitting, “well, we don't really know what this update fixed,” he analyzed Apple’s security releases page, pointing out that while Apple merely says it “has no published CVE entries” that this may not reveal much. Adrian Kingsley-Hughes, the brilliant Contributing Writer at ZDNet has been looking into things. There’s been further discussion of exactly what is in this new release. MORE FROM FORBES Apple iOS 17.2.1 Surprise iPhone Software Release: Should You Upgrade? By David Phelanĭecember 23 update. Please also check out my post on whether you should upgrade or not, which you’ll find here. After all, iOS 17.2.1 was something of a surprise when it appeared this week. Let’s hope so.Īll the while, Apple is continuing to test iOS 17.3 in beta, expected to land in January next year and it’s likely to be the next release. But it’s still a strong possibility that users everywhere could see less trouble from battery drain. In Japan and China, the notes mention that “This update addresses an issue where the battery may drain quickly under certain conditions.” YouTuber Brandon Butch spotted this and has now updated what was said with the note that “This was not a bug or glitch, as Apple also published the same release notes on their site.”ĭoes this guarantee better battery life in other places? No, because it’s possible that it was something in the coding unique to Chinese and Japanese iPhones that caused the glitch. First of all, it’s reported by MacRumors that the release notes are not the same across all territories. Some intriguing extra details have emerged about these latest updates, even though Apple has remained tight-lipped about exactly what is in them in terms of security updates. I’ll be monitoring how both the new updates are received, so please check back Thursday, when I’ll assess whether you should upgrade to the new version or not.ĭecember 22 update. It’s worth noting that iOS 17.2.1 was not released as a Rapid Security Response, which could indicate that the update is purely for bug fixes, not security issues. To track the latest updates for iOS, you can build the PyTorch iOS libraries from the source code.It doesn’t clarify whether the security issues are common to both releases, though an overlap seems probable at least. Learn how to add the model in an iOS project and use PyTorch pod for iOS. Learn how to convert the model to TorchScipt and (optional) optimize it for mobile apps. Learn how to reduce the model size and make it run faster without losing much on accuracy. Learn how to fuse a list of PyTorch modules into a single module to reduce the model size before quantization. List of recipes for performance optimizations for using PyTorch on Mobile. PyTorch iOS Tutorial and Recipes Image Segmentation DeepLabV3 on iOSĪ comprehensive step-by-step tutorial on how to prepare and run the PyTorch DeepLabV3 image segmentation model on iOS. TorchVideo demonstrates how to use a pre-trained video classification model, available at the newly released PyTorchVideo, on iOS to see video classification results, updated per second while the video plays, on tested videos, videos from the Photos library, or even real-time videos. Speech Recognition demonstrates how to convert Facebook AI’s wav2vec 2.0, one of the leading models in speech recognition, to TorchScript and how to use the scripted model in an iOS app to perform speech recognition. ![]() Vision Transformer demonstrates how to use Facebook’s latest Vision Transformer DeiT model to do image classification, and how convert another Vision Transformer model and use it in an iOS app to perform handwritten digit recognition. Question Answering demonstrates how to convert a powerful transformer QA model and use the model in an iOS app to answer questions about PyTorch Mobile and more. Neural Machine Translation demonstrates how to convert a sequence-to-sequence neural machine translation model trained with the code in the PyTorch NMT tutorial and use the model in an iOS app to do French-English translation. Object Detection demonstrates how to convert the popular YOLOv5 model and use it on an iOS app that detects objects from pictures in your photos, taken with camera, or with live camera. Image Segmentation demonstrates a Python script that converts the PyTorch DeepLabV3 model for mobile apps to use and an iOS app that uses the model to segment images. More PyTorch iOS Demo Apps Image Segmentation And a text-based app that uses a text classification model to predict the topic from the input string. A camera app that runs a quantized model to predict the images coming from device’s rear-facing camera in real time. prefix ( 3 ) PyTorch Demo Appįor more complex use cases, we recommend to check out the PyTorch demo application. Var normalizedBuffer : = ( repeating : 0, count : w * h * 3 ) // normalize the pixel buffer // see for more detail for i in 0. ![]()
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