mjf-su/baseVLA
4B • Updated • 27
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} | b93ab660-7e6d-40b3-b122-c24244f871da__04 | hf://datasets/tom-jerry-123/Physical-AI-AV-US@5d5f3a0ec4be19741ac71769a1e2454c10e2146e/shards/train-00000-of-00100.tar | |
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} | 0b3df5fd-5abe-4a15-91e4-62c04395f80c__02 | hf://datasets/tom-jerry-123/Physical-AI-AV-US@5d5f3a0ec4be19741ac71769a1e2454c10e2146e/shards/train-00000-of-00100.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 2,789,773 samples from 150 000
driving scenes (18 seconds per scene, sampled at 1 Hz) recorded in the
United States.
Format
WebDataset — 100 uncompressed .tar shards,
each containing pairs of files per sample:
| Entry | Description |
|---|---|
{key}.png |
Front-facing wide-angle camera frame (640 × 360 px) |
{key}.json |
Metadata (see schema below) |
Key format: {scene_id}__{sample_idx:02d}
Shard assignment: sha256(scene_id) % 100 — 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 1–18 — which second within the 18-second 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]] — past trajectory [[x,y,yaw], ...], 3 entries: [-2s, -1s, 0s (anchor)]",
"waypoints": "list[list[float]] — future trajectory [[x,y,yaw], ...], 4 entries at ~2s steps",
"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..00099}-of-00100.tar").shuffle(1000)
for sample in ds:
img = Image.open(io.BytesIO(sample["png"]))
meta = json.loads(sample["json"])
# meta["waypoints"] → [[x,y,yaw], ...] × 4 future steps
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
- Downloads last month
- 153
Size of the auto-converted Parquet files (First 5GB):
4.97 GB
Number of rows (First 5GB):
80,600
Estimated number of rows:
2,330,889
Models trained or fine-tuned on tom-jerry-123/Physical-AI-AV-US