Find the foreground, refine the edges, and take the original detail with you.
U²-NetP · 320×320 inference · single-thread CPU/WASM · measuring assets.
Apache-2.0 model; MIT runtime. Model license. Desktop Chrome tested; low-memory/mobile and WebGPU are unvalidated.
Hair, whiskers, glass and low-contrast edges need review. Upscaling a small mask cannot restore missing detail.
PNG, JPEG, WebP · 30 MiB · 20 MP · 8,192 px/side · no animated images
Choose an image and explicitly load the model.
No image is sent to a processing service.
Output uses original oriented dimensions and image colors, with your mask for alpha. JPEG always flattens onto the replacement color.
The lightweight foreground model was reviewed on a NASA portrait, coffee cup and cat image. It can retain background near the portrait, miss parts of the spoon, and lose whiskers/fur detail. Use compare, erase and restore; no perfect-edge or transparent-object promise is made.
Image dimensions are checked before decode and again after browser orientation. One image at a time; no batch queue. Original colors are used at export, not an enlarged 320×320 inference image. Model loading is explicit; there is no cloud fallback, analytics or persistent image/mask history.
Clear session closes the image bitmap, terminates the worker, and drops masks/history/export URLs. It cannot erase OS memory or previously saved files. Ordinary application/model requests still reach the host.
Choose a still PNG, JPEG or WebP, explicitly load U²-NetP, then run foreground inference. It uses a 320×320 preview and reconstructs alpha at the original oriented dimensions. Review residual background with compare and erase.