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omlab/OmTrackVLA-0.6B
OmTrackVLA-0.6B is a other model from omlab. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
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Downloads · 30 days
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From the Hugging Face model README
Visual Navigation & Following for Everyone.
OmTrackVLA is a fully open-source Vision-Language-Action (VLA) stack that turns monocular video and natural-language instructions into actionable, short-horizon waypoints.
While we explore massive backbones (8B/30B) internally, this repository is dedicated to democratizing embodied AI. We have intentionally released our highly efficient 0.6B checkpoint along with the full training pipeline.
Acknowledgment: OmTrackVLA builds on the ideas introduced by the original TrackVLA project. Their partially-open release inspired this community-driven effort to keep the ecosystem open so researchers and developers can continue improving the stack together.
The system processes video history and text instructions to predict future waypoints. Below are examples of the tracker in action:
<div align="center"> <img src="ex1.gif" width="45%" alt="Tracked clip 1" /> <img src="ex2.gif" width="45%" alt="Tracked clip 2" /> </div>This directory contains the HuggingFace-friendly export of the OmTrackVLA planner.
Full project (code, datasets, training pipeline): https://github.com/om-ai-lab/OmTrackVLA
from transformers import AutoModel
model = AutoModel.from_pretrained("omlab/OmTrackVLA-0.6B").eval()
OmTrackVLA GitHub Repository
Full Project Documentation
trained_agent.py prefers HuggingFace weights when either env var is set:
HF_MODEL_DIR=/abs/path/to/open_trackvla_hf (already downloaded)HF_MODEL_ID=omlab/OmTrackVLA-0.6B (auto-download via huggingface_hub)Example:
HF_MODEL_ID=omlab/OmTrackVLA-0.6B bash eval.sh