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Easy Vitpose

Easy Vitpose is an open-source toolkit that provides pretrained ViTPose models and inference tools for 2D human and animal pose estimation. It processes images, videos and webcam input, supports multiple skeleton and dataset formats, and can produce visual results or JSON keypoints.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Easy Vitpose — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Easy Vitpose when a computer-vision workflow needs 2D human or animal keypoints and you want to select among supported ViTPose sizes and skeleton formats.

Who it is for

  • Computer-vision developers adding human pose estimation to image or video workflows.
  • Researchers and developers exploring animal pose estimation with supported checkpoints.
  • Teams that need keypoint outputs in one of the supported skeleton formats.

Documented uses

  • Estimate pose keypoints from an image, video or webcam stream.
  • Track detected subjects across ordered video frames with SORT.
  • Save rendered results or keypoint data as JSON.
  • Convert a ViTPose checkpoint for an ONNX or TensorRT inference workflow.
Before you choose

Requirements & limits

  • The repository asks users to install PyTorch greater than 2.0 with CUDA or MPS support themselves.
  • Four model sizes are listed: small, base, large and huge.
  • Available dataset and skeleton choices include COCO, COCO + FEET, COCO WholeBody, MPII, APT36k, AP10k and AIC.
  • ONNX and TensorRT use requires converting an appropriate PyTorch checkpoint with the provided export script.
  • The README warns that YOLOv8 does not provide complete animal detection; a custom detector may be needed for a particular animal.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Easy Vitpose and its requirements

Prepare the repository environment before obtaining a model checkpoint and running inference.

Before you start

  • Git and Python package installation tools.
  • PyTorch greater than 2.0 with CUDA or MPS support, installed separately.

Steps

  1. Clone the Easy Vitpose repository and change into its directory.

  2. Install the project in editable mode with pip.

  3. Install the packages listed in requirements.txt.

Expected outcome

The repository package and its listed requirements are installed; the README’s next stage is downloading a model checkpoint.

Guide limitations

  • This setup sequence does not cover downloading a checkpoint or running inference.
  • The README also directs users to check requirements_gpu.txt.
Documentation 1 ↗
Source-based practical guide

Convert a PyTorch checkpoint for ONNX or TensorRT inference

Use the repository’s documented export script to convert a suitable ViTPose checkpoint.

Before you start

  • A downloaded PyTorch model checkpoint.
  • The repository and its installation requirements.
  • A model size: s, b, l or h.

Steps

  1. Download the appropriate PyTorch checkpoint for the target dataset and architecture.

  2. Use export.py with the checkpoint path and matching model name; optionally specify an output path and dataset.

  3. Use the resulting converted model in an ONNX or TensorRT inference workflow.

Expected outcome

A converted checkpoint intended for inference through ONNX or TensorRT.

Guide limitations

  • The README identifies the conversion command and formats but does not document a specific deployment configuration.
  • This guide does not cover converting the YOLOv8 detection model.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Easy VitposeEstimate pose keypoints from an image, video or webcam stream.Cloud
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
JunkyByte/easy_ViTPose · Official homepage title · retained in its source language
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