Datasets:
Search is not available for this dataset
sample_id string | L int32 | n_blocks int32 | input_ids unknown | input_ids_shape list | labels unknown | labels_shape list | rbi unknown | rbi_shape list | turn unknown | turn_shape list | scaffold unknown | scaffold_shape list | weight_vec unknown | weight_vec_shape list | block_alpha unknown | block_alpha_shape list | block_beta unknown | block_beta_shape list | position_ids unknown | position_ids_shape list | vision_mask unknown | vision_mask_shape list | pixel_values unknown | pixel_values_shape list | image_grid_thw unknown | image_grid_thw_shape list |
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End of preview. Expand in Data Studio
WOD-E2E Fast-dDrive SASD 50k — TPU-ready training shards
50,331 frames (32 of the 263 WOD-E2E train shards) pre-tokenized into the Fast-dDrive
Section-Aware Structured Diffusion (SASD) format, packaged as 787 Apache Parquet shards
(~22 GB) for multi-host TPU training (grain / datasets / MaxText hf data path). The
TPU side needs no tokenizer, processor, or torch — it loads arrays and trains.
⚠️ PRIVATE / license-restricted. Derived from the Waymo Open Dataset; shared privately under the WOD License (no redistribution). Do not make public.
Honest limitations
- Pseudo text labels:
trajectory+future_meta_behaviorderive from real GT / ego-intent;critical_objects+explanationare heuristic pseudo-labels (WOD-E2E ships no text targets). - Subset: 50k of ~420k train frames. The full split runs through the identical pipeline
(
jax_ddrive/scripts/convert_subset_parallel.py --shards 263). - Uniform shapes: L=1184, n_blocks=7, pixel_values (672, 1176) — trivial batching.
Schema & reconstruction
Identical to kaiwen2/wod-e2e-fast-ddrive-sasd — see dataset_info_train.json for array_dtypes.
Each array column is a little-endian binary blob + a <name>_shape list:
import numpy as np, pyarrow.parquet as pq, json
ADT = json.load(open("dataset_info_train.json"))["array_dtypes"]
row = pq.read_table("train-00000-of-00787.parquet").to_pylist()[0]
dec = lambda n: np.frombuffer(row[n], np.dtype(ADT[n])).reshape(row[n+"_shape"])
input_ids, pixel_values = dec("input_ids"), dec("pixel_values")
Columns: input_ids[L] i64, labels[L] i64, rbi[L] i32, turn[L] i32, scaffold[L] bool,
weight_vec[L] f32, block_alpha/beta[n_blocks] f32, position_ids[3,L] i32,
vision_mask[L] bool, pixel_values[N,1176] f16, image_grid_thw[n_img,3] i64.
Provenance
3-stage pipeline (autovla convert_wod_e2e.py → ddrive prep_train_jax.py →
prep_to_parquet.py), orchestrated by convert_subset_parallel.py. Model:
Efficient-Large-Model/Fast-dDrive (Qwen2.5-VL-3B block-diffusion VLA).
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