Update data card: forklift_collision packed to 2,989 shards / 6.80 TiB; fire downscoped to ~432 shards / ~1.93 TiB; artifacts tier totals 16.33 TiB

README.md

@@ -39,7 +39,7 @@ The release is packaged as standard [WebDataset](https://github.com/webdataset/w
 The dataset is published in two complementary tiers under the same repository, joinable per-sample on `run_id`:
 
 - The **RGB tier** (`rgb/`) contains the photoreal RGB video for every camera in every run, plus per-run metadata files (`meta.json` and `metadata.txt`) embedded inside each shard. It is 459 shards totaling approximately 2.24 TiB, and is the right entry point for video generation, video understanding, and any workflow that does not need pixel-level supervision.
-- The **artifacts tier** (`artifacts/`) contains the full annotation stack for the same runs: metric depth, colorized instance segmentation, shaded segmentation, and canny edges as MP4 videos; the raw per-frame integer-id instance-segmentation arrays consolidated to a single compressed NPZ per camera; the per-camera consolidated `camera_params.jsonl` and `object_detection.jsonl` files with two- and three-dimensional bounding boxes and per-frame intrinsics and extrinsics; and the source RGB MP4s repeated in the same shard so that the artifacts tier is self-contained. The artifacts tier totals approximately 13 TiB across all four scenarios.
+- The **artifacts tier** (`artifacts/`) contains the full annotation stack for the same runs: metric depth, colorized instance segmentation, shaded segmentation, and canny edges as MP4 videos; the raw per-frame integer-id instance-segmentation arrays consolidated to a single compressed NPZ per camera; the per-camera consolidated `camera_params.jsonl` and `object_detection.jsonl` files with two- and three-dimensional bounding boxes and per-frame intrinsics and extrinsics; and the source RGB MP4s repeated in the same shard so that the artifacts tier is self-contained. The artifacts tier totals approximately 16.33 TiB across all four scenarios.
 
 Two small Parquet indexes — `metadata/runs.parquet` (one row per run) and `metadata/clips.parquet` (one row per camera-clip) — enable filtering by scenario, seed, camera, or source kind without opening a single shard.
 
@@ -72,7 +72,7 @@ Two small Parquet indexes — `metadata/runs.parquet` (one row per run) and `met
 
 - *Record count.* 122,967 camera-clips drawn from 29,195 multi-camera simulation runs (WebDataset samples). Aggregate footage is approximately 412 hours.
 - *Feature count.* Per camera-clip, the dataset provides 5 video modalities (RGB, depth, instance segmentation, shaded segmentation, canny edges), 1 raw integer-id NPZ, and 2 structured-annotation JSONL files; per frame, the structured annotations contain 2D bounding boxes (both tight and loose axis-aligned), 3D oriented bounding boxes, and full camera intrinsics and extrinsics.
-- *Total data storage.* Approximately 15.2 TiB: the RGB tier is approximately 2.24 TiB (459 shards) and the artifacts tier is approximately 13 TiB (~3,130 shards).
+- *Total data storage.* Approximately 18.57 TiB: the RGB tier is approximately 2.24 TiB (459 shards) and the artifacts tier is approximately 16.33 TiB (5,261 shards).
 
 ## Why this dataset
 
@@ -98,11 +98,11 @@ The RGB and artifact tiers per scenario are summarized below.
 | Scenario | RGB path | RGB shards | RGB size | Artifacts path | Artifact shards | Artifact size |
 |---|---|---:|---:|---|---:|---:|
 | Forklift–human near-miss | `rgb/forklift_human_nearmiss/` | 113 | 549 GiB | `artifacts/forklift_human_nearmiss/` | 547 | 2.46 TiB |
-| Warehouse fire | `rgb/warehouse_fire/` | 125 | 619 GiB | `artifacts/warehouse_fire/` | ~470 | ~2.1 TiB |
-| Forklift–shelf collision | `rgb/forklift_shelf_collision/` | 114 | 559 GiB | `artifacts/forklift_shelf_collision/` | ~820 | ~3.4 TiB |
-| Warehouse box pickup | `rgb/warehouse_box_pickup/` | 107 | 520 GiB | `artifacts/warehouse_box_pickup/` | 1293 | 5.14 TiB |
+| Warehouse fire | `rgb/warehouse_fire/` | 125 | 619 GiB | `artifacts/warehouse_fire/` | ~432 | ~1.93 TiB |
+| Forklift–shelf collision | `rgb/forklift_shelf_collision/` | 114 | 559 GiB | `artifacts/forklift_shelf_collision/` | 2,989 | 6.80 TiB |
+| Warehouse box pickup | `rgb/warehouse_box_pickup/` | 107 | 520 GiB | `artifacts/warehouse_box_pickup/` | 1,293 | 5.14 TiB |
 
-The RGB tier totals approximately 2.24 TiB; the artifacts tier totals approximately 13 TiB. To pull a scenario together with its annotations, include both `rgb/<scenario>/**` and `artifacts/<scenario>/**`. For streaming pipelines that never materialize the data on disk, see [Loading examples](#loading-examples).
+The RGB tier totals approximately 2.24 TiB; the artifacts tier totals approximately 16.33 TiB. To pull a scenario together with its annotations, include both `rgb/<scenario>/**` and `artifacts/<scenario>/**`. For streaming pipelines that never materialize the data on disk, see [Loading examples](#loading-examples).
 
 ## Scenarios
 
@@ -193,11 +193,11 @@ nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes/
 │   ├── warehouse_fire/             (125 shards)
 │   ├── forklift_shelf_collision/   (114 shards)
 │   └── warehouse_box_pickup/       (107 shards)
-└── artifacts/                  ← artifacts tier (~13 TiB)
+└── artifacts/                  ← artifacts tier (~16.33 TiB)
     ├── forklift_human_nearmiss/    (547 shards, ~5GBs each)
-    ├── warehouse_fire/             (~470 shards)
-    ├── forklift_shelf_collision/   (~820 shards)
-    └── warehouse_box_pickup/       (1293 shards)
+    ├── warehouse_fire/             (~432 shards)
+    ├── forklift_shelf_collision/   (2,989 shards)
+    └── warehouse_box_pickup/       (1,293 shards)
 ```
 
 Each `.tar` is a WebDataset archive. The 2 tiers share the same `__key__` (the `run_id`) so that joining them only requires opening the corresponding shard in each tier.