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Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. Sapiens-1B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic.

Image segmentation
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A SegFormer-B4 model fine-tuned for human parsing with 18 semantic classes, optimized for fashion and virtual try-on applications. This model segments human images into 18 semantic categories including body parts (face, hair, arms, hands, legs, feet, torso), clothing items (top, dress, skirt, pants, belt, scarf), and accessories (bag, hat, glasses, jewelry). - **Architecture**: SegFormer-B4 (MIT-B4 encoder + MLP decoder) - **Input Size**: 384 x 576 (width x height) - **Output**: 18-class semantic segmentation mask - **Base Model**: nvidia/mit-b4

Image segmentation
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toonout

joelseytre

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Image segmentation
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face-parsing

jonathandinu

> ONNX model for web inference contributed by Xenova. Exhaustive list of labels can be extracted from config.json. Since p5.js uses an animation loop abstraction, we need to take care loading the model and making predictions.

Image segmentation
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