App Icon Badger vs AnnotateIt AI

App Icon Badger Add DEV, STAGE, or BETA badges to your app icons in seconds - no scripts, no setup. Just drop, badge, export. App Icon Badger is a lightweight, no-fuss utility built for developers who want a simple way to mark app builds for different environments. Whether you're shipping a test build to a client or want to avoid launching the wrong version to production, this tool helps you visually tag your icons with custom badges - fast. Why Developers Love It No CI/CD Integration Required No pipelines. No plugins. Just drag and badge. Instant Badge Preview See exactly what your icon will look like - in real-time. Environment Labels Made Easy Choose from presets like DEV, STAGE and many more. Customizable Design Pick shield colors, text, and position to suit your style or workflow. Use It. Badge It. Done. This isn’t another complex build tool. App Icon Badger is made for quick use - before release, during QA, or while testing with clients. No installs. No syncing. Just results. Perfect for: - Indie devs & freelancers - Test builds & internal deployments - Teams who just want to see what’s what Badge responsibly. Never confuse your builds again.

Label images and video on your iPhone or iPad. Tap an object and on-device AI outlines it — no account, no upload, no internet required once you are set up. AnnotateIt AI is a local-first annotation tool for building computer vision datasets wherever you are. TAP TO SEGMENT Tap any object and Segment Anything draws its mask right on your device. Refine it with more taps, then keep it as an editable polygon or box. Built for touch: annotate with your finger or Apple Pencil. LABEL BY EXAMPLE Annotate a few objects by hand, and AnnotateIt finds similar objects across the rest of your images. Every suggestion waits for your review before it is saved — you stay in control of your data. VIDEO TRACKS Open a video, tap the objects you care about and turn them into tracks that keep one identity across frames. Step through the clip, adjust a box on a later frame and the frames in between are filled in for you. KEY FEATURES • Annotate images and video frames • Create bounding boxes, polygons, masks, keypoints, and classifications • On-device AI segmentation with one tap • Auto-annotate similar objects from your own examples, then review suggestions • Capture photos and video with your iPhone or iPad camera • Organize labels and classes for different project types • Check dataset quality: missing labels, shapes out of bounds, duplicates • Split into train, validation and test sets and save dataset versions • Undo and redo every edit • Import and export complete project archives • Ready-made demo projects to explore in one tap • Use the interface in English, Spanish, or Brazilian Portuguese BUILT FOR COMPUTER VISION • Object detection • Instance and semantic segmentation • Single-label, multi-label, and hierarchical classification • Keypoint and pose detection • Video annotation and object tracking FLEXIBLE EXPORTS Export labeled datasets to formats including YOLO, COCO, Pascal VOC, Datumaro and MOT, and save them to Files or share them to your Mac. Available formats depend on the project and annotation types. PRIVATE BY DESIGN Your projects, images, videos and annotations are stored on your device. The annotation workflow needs no account and nothing is uploaded to an external service: manual tools and on-device AI process your media locally. Network access is used only to download demo projects and to open links you choose. WORKS WITH ANNOTATEIT ON MAC Start a dataset in the field on your iPhone or iPad, then continue on the Mac app — with model training in Create ML and AI assistants — using the same project archive. AnnotateIt AI is built for machine learning engineers, computer vision researchers, students, field teams and anyone preparing visual datasets. AI-generated suggestions should be reviewed before inclusion in production datasets. Very large images and long videos may exceed what a mobile device can process; performance of AI-assisted features varies by device.