Ronaldo-GOAT/pose6daug
pose6daug Real-world Franka manipulation episodes with object-swap and action augmentation artifacts. 120 training episodes over 4 objects (blue_cup, green_pear, kanu, white_spray), dual ZED cameras (exo static + ego wrist-mounted). Layout Per-frame PNGs are packed into uncompressed tars per episode — the dataset has ~427k mask/plate frames and loose files hit Hugging Face's per-repo file recommendation and API rate limits hard. data/<object>/<NNNN>/ masks.tar… See the full description on the dataset page: https://huggingface.co/datasets/Ronaldo-GOAT/pose6daug.
0633
1#!/bin/bash2# Regenerate fingertip-tracking ego robot masks for all valid-80. 8-way (2/gpu).3R=/home/nvidia/jonghoon/rebuttal; PY_S2=/data/nvidia/gripper_augmentator/conda-envs/sam2/bin/python4LOG=/lp-dev/jonghoon/rebuttal_vace/mask_logs5python3 - <<'PY' > /lp-dev/jonghoon/rebuttal_vace/ft_jobs.txt6import json7sel=json.load(open("/lp-dev/jonghoon/rebuttal_data/valid80.json"))8for ds,s in sel.items():9 for ep in s["keep"]: print(ds, ep)10PY11mapfile -t J < /lp-dev/jonghoon/rebuttal_vace/ft_jobs.txt12NW=813worker(){ local wid=$1 gpu=$((wid%4)) i=014 for line in "${J[@]}"; do15 if [ $((i % NW)) -eq $wid ]; then16 set -- $line; DS=$1; EP=$217 WS=/lp-dev/jonghoon/rebuttal_pose/rlwrld_$DS; [ "$DS" = red_cleanser ] && WS=/lp-dev/jonghoon/rebuttal_pose/rlwrld18 if [ ! -e "$WS/mask_automation/$EP/masks/robot_ego_cad/000000.png" ]; then echo "[w$wid] $DS/$EP NO_CAD skip"; i=$((i+1)); continue; fi19 RL_WS=$WS RL_FRAMES_EGO=$WS/sam2_frames_ego RL_EVENTS_DIR=$WS/events \20 $PY_S2 $R/mask_automation/ego/sam2_robot_ego_ft.py $EP $gpu > $LOG/ft_${DS}_${EP}.log 2>&1 \21 && echo "[w$wid gpu$gpu] $DS/$EP OK" || echo "[w$wid] $DS/$EP FAILED"22 fi23 i=$((i+1))24 done }25pids=(); for w in $(seq 0 $((NW-1))); do worker $w & pids+=($!); done26for p in "${pids[@]}"; do wait $p; done27echo "=== FT MASK BATCH DONE $(date) ==="28