Lobe

Open Lobe, start a new project, and get to work in minutes. Pick a starter blueprint that fits your task—image tagging, marking objects, or general categorization—then drag in a folder of examples. Use the built-in tools to tag photos, draw boxes around what matters, and merge or rename labels as you learn more about your data. Add more samples on the fly from your files or webcam, and keep your set tidy by removing near-duplicates and fixing mislabeled items. Everything runs locally on macOS or Windows, so you can iterate wherever you’re working.

When you’re ready, click Train. Lobe runs an automatic training cycle and shows results you can understand at a glance. Browse predictions, see which labels are strong or weak, and jump directly to the mistakes. Correct those errors, add a few targeted examples, and train again. This feedback loop is fast: review, tweak, retrain, repeat. Keep versions with notes so you can compare improvements over time and confidently roll back if a change doesn’t help. Use uncertainty to guide your next labeling pass—prioritize the images the model isn’t sure about to get the biggest lift with the least effort.

Test your model on real inputs before you share it. Drop in new images, stream from a camera, or batch-check a folder to make sure results hold up outside your training set. Once you’re satisfied, package the model for use in your app or workflow. Hand the project folder to a teammate, or keep it in source control alongside your dataset and notes. Because the interface and files are consistent across Mac and Windows, teams can switch machines without friction.

Use Lobe to speed up practical jobs: sort product photos into categories for an online shop; highlight defects in quality-control snapshots; flag unsafe imagery for a community; auto-tag marketing assets so creatives find the right file faster; build a classroom demo that teaches the ML lifecycle in a single afternoon; prototype a visual search feature for a pitch; or add simple object awareness to a desktop tool. A reliable routine looks like this: define 3–10 clear labels, gather 30–100 examples per label, train, review errors, add targeted samples, retrain, and repeat until results stabilize. Keep labels consistent, balance your classes, and test with fresh images every cycle. With that workflow, you’ll move from idea to a dependable, shippable model in short, focused sessions.