Datasets:

s3no-benchmark
/

suboff_data

@@ -9,14 +9,14 @@ tags:
 - point-cloud
 - submarine
 - benchmark
-pretty_name: Suboff Demo Dataset
 size_categories:
-- n<1K
 ---
-# Suboff Demo Dataset
-This public dataset contains the Suboff demo training subset and the public validation inputs used by the demo leaderboard. It is intended for checking data loading, model input construction, local inference, and end-to-end leaderboard submission before working with the full benchmark.
 The data represents steady-state surface pressure over parameterized submarine hull geometries. Each geometry is evaluated under multiple Reynolds-number conditions. The model input is an irregular surface point cloud plus flow-condition features; the prediction target is pressure or pressure coefficient at each surface point.
@@ -32,12 +32,12 @@ data/
         +-- all_zones_combined.npy
 ```
-This demo split contains:
 | Split | Label availability | Geometries | Conditions per geometry | Files |
 | --- | --- | ---: | ---: | ---: |
-| Train demo | Public pressure labels | 2 | 6 | 12 |
-| Validation input demo | No pressure labels | 1 | 6 | 6 |
 Training `.npy` files are approximately 8 MB each. Validation-input `.npy` files are smaller because the pressure column has been removed.
@@ -74,7 +74,7 @@ The four validation-input columns are:
 | 2 | `z` | Surface point z-coordinate |
 | 3 | `zone_id` | Integer-like surface zone identifier |
-The point count `N` can vary by sample. The demo files are irregular surface point clouds and should not be treated as structured grids.
 ## Flow Conditions
@@ -183,7 +183,7 @@ The point order must match the corresponding `data/validation_input/<sample-id>/
 ## Public and Private Data Boundary
-This repository is public. It contains training-demo files with pressure values and validation-input files without pressure values. The private validation/demo-truth files are stored separately in `Hanqqq/suboff_val_demo` and should not be copied into this public repository.
 For a leaderboard workflow:
@@ -195,4 +195,4 @@ For a leaderboard workflow:
 This dataset repository is released under the Apache License 2.0 as indicated in the Dataset Card metadata.
-If you use this demo data in a benchmark, report the exact split and the condition mapping above so results remain reproducible.

 - point-cloud
 - submarine
 - benchmark
+pretty_name: Suboff Dataset
 size_categories:
+- 1K<n<10K
 ---
+# Suboff Dataset
+This public dataset contains the Suboff training split and the public validation inputs used by the leaderboard. It is intended for model training, local inference, and end-to-end leaderboard submission.
 The data represents steady-state surface pressure over parameterized submarine hull geometries. Each geometry is evaluated under multiple Reynolds-number conditions. The model input is an irregular surface point cloud plus flow-condition features; the prediction target is pressure or pressure coefficient at each surface point.
         +-- all_zones_combined.npy
 ```
+This split contains:
 | Split | Label availability | Geometries | Conditions per geometry | Files |
 | --- | --- | ---: | ---: | ---: |
+| Train | Public pressure labels | 158 | 6 | 948 |
+| Validation input | No pressure labels | 39 | 6 | 234 |
 Training `.npy` files are approximately 8 MB each. Validation-input `.npy` files are smaller because the pressure column has been removed.
 | 2 | `z` | Surface point z-coordinate |
 | 3 | `zone_id` | Integer-like surface zone identifier |
+The point count `N` can vary by sample. These files are irregular surface point clouds and should not be treated as structured grids.
 ## Flow Conditions
 ## Public and Private Data Boundary
+This repository is public. It contains training files with pressure values and validation-input files without pressure values. The private validation-truth files are stored separately in `s3no-benchmark/suboff_val_demo` and should not be copied into this public repository.
 For a leaderboard workflow:
 This dataset repository is released under the Apache License 2.0 as indicated in the Dataset Card metadata.
+If you use this data in a benchmark, report the exact split and the condition mapping above so results remain reproducible.

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