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: 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

Requirements

  • Packages: torch, torchvision, transformers

  • CPU support

    • yes

  • GPU support

    • yes

Example usage

 1import fiftyone as fo
 2import fiftyone.zoo as foz
 3
 4dataset = foz.load_zoo_dataset(
 5    "coco-2017",
 6    split="validation",
 7    dataset_name=fo.get_default_dataset_name(),
 8    max_samples=50,
 9    shuffle=True,
10)
11
12model = foz.load_zoo_model("vitpose-plus-small-torch")
13
14dataset.apply_model(model, prompt_field="ground_truth", label_field="predictions")
15
16session = fo.launch_app(dataset)