---
library_name: pytorch
tags:
- robotics
- vggt-omega
- oxe
- mimicgen
- robocasa365
- H4
---

# VGGT-Omega 1B 256 OXE/MimicGen/RoboCasa365 Step 51000

Checkpoint uploaded from the CVLAB1 DA3/VGGT-Omega robot training run.

## Files

- `0051000.pt` - consolidated checkpoint for resume/eval, saved before eval at step 51,000.
- `config.yaml` - resolved training config saved by the run.

## Run

- W&B run id: `8gxiadnc`
- W&B run name: `[VGGTOMEGA]_hf23000_H4_cvlab1_8gpu_mb28acc1_zero2_ckptfirst_evalmb1_eval1000_20260522_003836`
- Source local checkpoint path: `/NHNHOME/WORKSPACE/0226010404_A/CVLAB/CVLAB1/jisang/3DA_unified/logs/train/[VGGTOMEGA]_resume8gxiadnc_officialteacher_ckpt34000_H4_cvlab1_8gpu_mb28acc1_zero2_eval1000_20260522_124402/robot-20260522-124541/checkpoints/0051000.pt`
- Saved time: `2026-05-23 04:35:30 KST`
- Checkpoint size: 2.91 GiB

## Key settings

- Backbone: `vggt_omega`
- Teacher checkpoint: pristine VGGT-Omega text checkpoint (`vggt_omega_1b_256_text.pt`)
- Student resume base: step 34,000 from the same W&B run lineage
- H choices: `[4]`
- `deep_temporal_causal_mask=true`
- Dataset mixture: OXE / MimicGen / RoboCasa365
- Training batch: 8 GPUs, micro batch 28, grad accum 1, ZeRO-2
- Eval/checkpoint interval: 1000 steps

## Step 51000 console snapshot

- train total: `0.3854`
- train action L1: `0.0742`
- train depth loss: `0.0386`
- train rel@1/5/10: `0.402 / 0.755 / 0.889`
- eval unified H4 L1 norm: `0.1940`
- eval unified rel@5/10: `0.504 / 0.678`
- eval samples: `104`