# vitpose-plus-small-torch Small ViTPose+ with 30M parameters using mixture-of-experts for multi-dataset training. Achieves 68.7 AP through dataset-specific adaptation. Lightweight MOE architecture enables efficient pose estimation across diverse human pose datasets.. **Details** - Model name: `vitpose-plus-small-torch` - Model source: [https://github.com/ViTAE-Transformer/ViTPose](https://github.com/ViTAE-Transformer/ViTPose) - Model author: Yufei Xu, et al. - Model license: Apache 2.0 - Model size: 126.48 MB - Exposes embeddings? no - Tags: `keypoints, coco, torch, transformers, pose-estimation, official` **Requirements** - Packages: `torch, torchvision, transformers` - CPU support - yes - GPU support - yes **Example usage** ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset( "coco-2017", split="validation", dataset_name=fo.get_default_dataset_name(), max_samples=50, shuffle=True, ) model = foz.load_zoo_model("vitpose-plus-small-torch") dataset.apply_model(model, prompt_field="ground_truth", label_field="predictions") session = fo.launch_app(dataset) ```