Screenia vs TFLite LiteRT TensorFlow Test

Screenia is a cross-platform tool for taking and editing screenshots. It also provides cloud storage and allows you to share links to your screenshots. Available for macOS, iOS, web, and other platforms. Free version: • Capture fullscreen or fragment • Edit screenshots with pencil, text, arrows, and shapes • Save 100 screenshots on the cloud • Customize hotkeys • Sharable screenshot links • Original screenshot quality • 14-day screenshot storing period • Screenshot collection • Shared with me history Premium version: • Capture fullscreen or fragment • Edit screenshots with pencil, text, arrows, and shapes • Save unlimited screenshot amount on the cloud • Customize hotkeys • Sharable screenshot links • Original screenshot quality • Unlimited screenshot storing period • Screenshot collection • Shared with me history • Device metadata • Screenshot passphrase protection ____________ Screenia can be used without an activated subscription, with limited features. Premium version can be enabled with paid: • Monthly subscription $7.99 • Seasonal subscription $6.99 / per month ($20.99 total cost) • Yearly subscription $4.99 / per month ($59.99 total cost) Subscription will be charged to your credit card through your App Store account. Your subscription will automatically renew unless canceled at least 24 hours before the end of the current period. Manage your subscription in the Account Settings on the App Store after purchase. Follow us: Facebook: https://www.facebook.com/screeniaapp Twitter: https://twitter.com/ScreeniaAPP LinkedIn: https://www.linkedin.com/company/screeniaapp Contact us: hello@screenia.app Legal: Privacy Policy: https://screenia.app/privacy-policy Terms of Use: https://screenia.app/terms-and-conditions

How fast does your .tflite model actually run on this iPhone? Pick CPU, GPU or Neural Engine. Get real latency and QPS. No Mac, no Xcode project, no Bazel build, no cloud, no account. Open a .tflite file from the Files app, pick an accelerator, run it. A real measurement on the exact device you care about, in about a minute — no Bazel workspace, no benchmark tool built from source. PICK THE ACCELERATOR - Neural Engine — attaches the Core ML delegate, so supported operators run on the Apple Neural Engine - GPU — attaches the GPU delegate, backed by Metal - CPU — the plain interpreter, with a thread count you choose: 1, 2, 4, 6 or 8 One configuration per run: run it, change the delegate or thread count, load it again, compare the numbers yourself. WHAT YOU GET - Latency: mean, min and max, in milliseconds - Queries per second - Total queries completed and total run duration - A query count you set per run, from 50 to 5,000 invocations Mean, min and max time the model inference call only — no pre-processing, no post-processing. Duration covers the whole loop. The screen stays awake during a run. WHAT YOU SEE ABOUT THE MODEL - Every input and output tensor: index, name, shape and data type (uInt8, int32, float16, float32 and the rest) - Model file size and framework - Live app memory usage against the memory available to the app, refreshed while the model runs - A device tab with model identifier, system version, disk space, and the exact TensorFlow Lite runtime version this build links against A MobileNet model ships in the app and loads on launch, so you see a real measurement before importing your own. READ THIS BEFORE YOU TRUST A NUMBER Inference runs on a zeroed dummy input. The app fills every input tensor with zeros and invokes the model, so what you measure is the compute cost of the graph on the accelerator you picked — latency and throughput, nothing else. It is not an accuracy test. It will not tell you whether your model gives the correct answer, and it does not show output tensor values. Ask it "how fast", not "how correct". What it does not do, stated up front: no side-by-side accelerator comparison in one run, no per-layer or per-operator profiling, no result export, no image or real-data input. It opens .tflite files only. PRIVACY Your model never leaves the device. Loading, inspection, inference and timing all happen locally on your iPhone or iPad. No account, no sign-in — your models and your results are never uploaded. TensorFlow Lite is now called LiteRT; the format and the .tflite extension are unchanged. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. Not affiliated with or endorsed by Google.