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} | fb2a3483-b5a9-407e-ac0f-a3e3460cd8c0__10 | hf://datasets/tom-jerry-123/Physical-AI-AV-DE@e8e589159f622b9909403edffab648c0ff07bad5/shards/train-00000-of-00010.tar | |
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} | ba87ef4b-bd9a-4d6d-8823-1bbd56946018__09 | hf://datasets/tom-jerry-123/Physical-AI-AV-DE@e8e589159f622b9909403edffab648c0ff07bad5/shards/train-00000-of-00010.tar |
End of preview. Expand in Data Studio
PhysicalAI-AV-SFT
Supervised fine-tuning (SFT) dataset for an autonomous-vehicle vision-language
waypoint-prediction model. Contains 324,105 samples from 150 000
driving scenes (18 seconds per scene, sampled at anchor times 2s..16s) recorded in the
United States.
Format
WebDataset — 10 uncompressed .tar shards,
each containing pairs of files per sample:
| Entry | Description |
|---|---|
{key}.jpg |
Front-facing wide-angle camera frame (JPEG quality 95, 640 × 360 px) |
{key}.json |
Metadata (see schema below) |
Key format: {scene_id}__{sample_idx:02d}
Shard assignment: sha256(scene_id) % 10 — all frames of a scene land
in the same shard, preventing scene leakage across train/eval splits.
Metadata schema ({key}.json)
{
"scene_id": "UUID string — identifies the driving scene",
"chunk_name": "chunk_XXXX — source data chunk",
"sample_idx": "int 2–16 — target second within the scene (15 anchors/scene)",
"global_idx": "int — globally unique datum index",
"target_t_rel_us": "int — timestamp relative to scene start (microseconds)",
"target_frame_index": "int — video frame index",
"egomotion": "list[list[float]] — full available past trajectory (incl. anchor), target-relative, 0.25s granularity",
"waypoints": "list[list[float]] — full available future trajectory, target-relative, 0.25s granularity",
"is_long_tail": "bool — long-tail driving scenario flag"
}
Coordinate convention: all x/y/yaw in the ego-vehicle frame at target time,
+x = forward, +y = left, yaw in radians CCW from forward.
Loading
import webdataset as wds, json
from PIL import Image
import io
# Local (after cloning the repo)
ds = wds.WebDataset("shards/train-{00000..00009}-of-00010.tar").shuffle(1000)
for sample in ds:
img = Image.open(io.BytesIO(sample["jpg"]))
meta = json.loads(sample["json"])
# meta["waypoints"] → full available future waypoints at 0.25s granularity
Shard index
index.parquet — one row per sample, columns:
key, shard, scene_id, chunk_name, sample_idx, global_idx,
target_t_rel_us, is_long_tail.
import pandas as pd
df = pd.read_parquet("index.parquet")
lt = df[df["is_long_tail"]] # long-tail subset
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