Add semantics NPZs for hssd-102343992; rewrite README to match paper

README.md

@@ -24,7 +24,7 @@ language:
 
 This is a **~500 MB sample** of the [Yonder](https://huggingface.co/datasets/astralhf/yonder)
 drone navigation dataset, intended for fast inspection by NeurIPS reviewers and others
-who want to verify the data format and content before downloading the full 3.8 TB release.
+who want to verify the data format and content before downloading the full ~3.3 TB release.
 
 ## What's included
 
@@ -32,17 +32,20 @@ who want to verify the data format and content before downloading the full 3.8 T
 - **50 consecutive waypoint NPZs** (`wp0000` through `wp0049`)
 - **All 12 yaw orientations** per waypoint
 - **All sensor modalities** present in the full dataset:
-  stereo RGB (left/right), forward depth, landing-camera, up-IR, down-IR,
-  360° LiDAR, position, orientation, IMU
-- ~50 × 10 MB ≈ **500 MB** total
+  stereo RGB (left/right), forward depth, landing camera, up-IR, down-IR,
+  360° LiDAR, position, orientation
+- **Semantic segmentation** for every waypoint (50 matching `*_semantics.npz` files)
+- ~50 × 10 MB sensor + 50 × ~25 KB semantics ≈ **500 MB** total
 
 ## What's NOT in this sample
 
-- **Semantic segmentation** — this particular scene does not have semantic annotations.
-  The full dataset includes semantics for 92 of 251 scenes; download the main repo and
-  filter by `manifest.json` `has_semantics: true` to obtain a semantics-bearing sample.
 - **Multiple scenes** — by design. This sample is a single-scene slice. The full
-  release spans 251 scenes (167 HSSD + 84 ReplicaCAD).
+  release spans **167 HSSD scenes**, all with semantic annotations. Other scene
+  sources (ReplicaCAD, Replica, HM3D) considered during early collection have been
+  excluded from the public release for license-compatibility reasons; see the
+  [main dataset card](https://huggingface.co/datasets/astralhf/yonder) for details.
+- **COCO bounding-box annotations** — derived programmatically from the semantic
+  channels and shipped per-scene under `annotations/` on the main repo.
 
 ## Quick start
 
@@ -52,22 +55,26 @@ import numpy as np
 
 local = snapshot_download(repo_id="astralhf/yonder-sample", repo_type="dataset")
 
-# Inspect the first waypoint
+# Sensor data
 data = np.load(f"{local}/hssd-102343992_wp0000.npz")
 print(sorted(data.keys())[:10])
 # ['down_ir', 'lidar360', 'orientation', 'position', 'up_ir',
 #  'yaw000_forward_depth', 'yaw000_landing_cam', 'yaw000_left_rgb',
 #  'yaw000_right_rgb', 'yaw001_forward_depth']
 
-# Shapes
 print("left_rgb yaw000:", data["yaw000_left_rgb"].shape, data["yaw000_left_rgb"].dtype)
 # left_rgb yaw000: (480, 640, 3) uint8
 print("forward_depth yaw000:", data["yaw000_forward_depth"].shape, data["yaw000_forward_depth"].dtype)
 # forward_depth yaw000: (480, 640) float16
 print("lidar360:", data["lidar360"].shape, data["lidar360"].dtype)
 # lidar360: (1024, 16) float32
-print("position:", data["position"])
-# position: (3,) float32 — Habitat-Sim world frame
+
+# Semantic segmentation (one file per waypoint, 12 yaw keys)
+sem = np.load(f"{local}/hssd-102343992_wp0000_semantics.npz")
+print(sorted(sem.keys())[:4])
+# ['yaw000_semantic', 'yaw030_semantic', 'yaw060_semantic', 'yaw090_semantic']
+print("semantic yaw000:", sem["yaw000_semantic"].shape, sem["yaw000_semantic"].dtype)
+# semantic yaw000: (480, 640) uint16  (per-pixel instance ID)
 ```
 
 ## Visualizing a frame
@@ -77,11 +84,14 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 data = np.load("hssd-102343992_wp0000.npz")
-fig, axes = plt.subplots(1, 3, figsize=(15, 5))
-axes[0].imshow(data["yaw000_left_rgb"]);   axes[0].set_title("Left RGB")
-axes[1].imshow(data["yaw000_right_rgb"]);  axes[1].set_title("Right RGB")
+sem  = np.load("hssd-102343992_wp0000_semantics.npz")
+fig, axes = plt.subplots(1, 4, figsize=(20, 5))
+axes[0].imshow(data["yaw000_left_rgb"]);                 axes[0].set_title("Left RGB")
+axes[1].imshow(data["yaw000_right_rgb"]);                axes[1].set_title("Right RGB")
 axes[2].imshow(data["yaw000_forward_depth"], cmap="plasma")
 axes[2].set_title("Forward depth (m)")
+axes[3].imshow(sem["yaw000_semantic"], cmap="tab20")
+axes[3].set_title("Semantic instance IDs")
 for a in axes: a.axis("off")
 plt.tight_layout(); plt.savefig("yonder_sample.png", dpi=150)
 ```
@@ -89,11 +99,15 @@ plt.tight_layout(); plt.savefig("yonder_sample.png", dpi=150)
 ## Going to the full dataset
 
 ```python
-# Single scene from the full repo (~25 GB)
+# Single scene from the full repo (~25 GB sensor + semantics)
 snapshot_download(
     repo_id="astralhf/yonder",
     repo_type="dataset",
-    allow_patterns="indoor/drone-data/augmented/hssd-102343992/*.npz",
+    allow_patterns=[
+        "indoor/drone-data/augmented/hssd-102343992/*.npz",
+        "semantics/hssd-102343992/*.npz",
+        "annotations/hssd-102343992/*.json",
+    ],
 )
 
 # Just the manifests (a few MB) to plan a custom download
@@ -114,7 +128,7 @@ licensing and Responsible AI considerations.
 
 ```bibtex
 @inproceedings{anonymous2026yonder,
-  title  = {Yonder: A 6.1M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap},
+  title  = {Yonder: A 4.65M-Frame Drone Navigation Dataset and the Cross-Simulator Generalization Gap},
   author = {Anonymous Author(s)},
   booktitle = {NeurIPS Datasets and Benchmarks Track},
   year   = {2026},

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