Update dataset card metadata - 2025-09-24T10:13:04.819790

README.md

@@ -10,151 +10,19 @@ tags:
 - aerial-imagery
 - orthomosaic
 - lighting-invariance
-- semantic-stability
+- representation-stability
 - vision-encoder
 - time-series
 - dinov2
 - dinov3
 - embeddings
 - multi-config
-pretty_name: Light Stable Semantics
+pretty_name: Light Stable Representations
 size_categories:
 - n<1K
-dataset_info:
-- config_name: default
-  features:
-  - name: idx
-    dtype: string
-  - name: image_t0
-    dtype: image
-  - name: image_t1
-    dtype: image
-  - name: image_t2
-    dtype: image
-  - name: canopy_height
-    dtype:
-      array2_d:
-        shape:
-        - 1024
-        - 1024
-        dtype: int32
-  splits:
-  - name: train
-    num_bytes: 4905235380
-    num_examples: 487
-  - name: test
-    num_bytes: 1221459061
-    num_examples: 122
-  download_size: 3688072446
-  dataset_size: 6126694441
-- config_name: dinov2_base
-  features:
-  - name: idx
-    dtype: string
-  - name: cls_t0
-    list: float32
-    length: 768
-  - name: cls_t1
-    list: float32
-    length: 768
-  - name: cls_t2
-    list: float32
-    length: 768
-  - name: patch_t0
-    dtype:
-      array2_d:
-        shape:
-        - 256
-        - 768
-        dtype: float32
-  - name: patch_t1
-    dtype:
-      array2_d:
-        shape:
-        - 256
-        - 768
-        dtype: float32
-  - name: patch_t2
-    dtype:
-      array2_d:
-        shape:
-        - 256
-        - 768
-        dtype: float32
-  splits:
-  - name: train
-    num_bytes: 1154971327
-    num_examples: 487
-  - name: test
-    num_bytes: 289335733
-    num_examples: 122
-  download_size: 1487171455
-  dataset_size: 1444307060
-- config_name: dinov3_sat
-  features:
-  - name: idx
-    dtype: string
-  - name: cls_t0
-    list: float32
-    length: 1024
-  - name: cls_t1
-    list: float32
-    length: 1024
-  - name: cls_t2
-    list: float32
-    length: 1024
-  - name: patch_t0
-    dtype:
-      array2_d:
-        shape:
-        - 196
-        - 1024
-        dtype: float32
-  - name: patch_t1
-    dtype:
-      array2_d:
-        shape:
-        - 196
-        - 1024
-        dtype: float32
-  - name: patch_t2
-    dtype:
-      array2_d:
-        shape:
-        - 196
-        - 1024
-        dtype: float32
-  splits:
-  - name: train
-    num_bytes: 1180053775
-    num_examples: 487
-  - name: test
-    num_bytes: 295619221
-    num_examples: 122
-  download_size: 1520934285
-  dataset_size: 1475672996
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
-- config_name: dinov2_base
-  data_files:
-  - split: train
-    path: dinov2_base/train-*
-  - split: test
-    path: dinov2_base/test-*
-- config_name: dinov3_sat
-  data_files:
-  - split: train
-    path: dinov3_sat/train-*
-  - split: test
-    path: dinov3_sat/test-*
 ---
 
-# Light Stable Semantics Dataset
+# Light Stable Representations Dataset
 
 ## Dataset Description
 
@@ -212,9 +80,9 @@ Pre-computed DINOv3 Large (ViT-L/16) embeddings with satellite pretraining:
 from datasets import load_dataset
 
 # Load specific configurations
-dataset_default = load_dataset("mpg-ranch/light-stable-semantics", "default")
-dataset_dinov2 = load_dataset("mpg-ranch/light-stable-semantics", "dinov2_base")
-dataset_dinov3 = load_dataset("mpg-ranch/light-stable-semantics", "dinov3_sat")
+dataset_default = load_dataset("mpg-ranch/drone-lsr", "default")
+dataset_dinov2 = load_dataset("mpg-ranch/drone-lsr", "dinov2_base")
+dataset_dinov3 = load_dataset("mpg-ranch/drone-lsr", "dinov3_sat")
 
 # Access raw imagery and canopy height
 sample_default = dataset_default['train'][0]
@@ -249,13 +117,13 @@ The dataset includes pre-computed embeddings from two state-of-the-art vision tr
 
 ### DINOv2 Base (`facebook/dinov2-base`)
 - **Architecture**: Vision Transformer Base with 14×14 patch size
-- **CLS Tokens**: 768-dimensional global feature vectors capturing scene-level semantics
+- **CLS Tokens**: 768-dimensional global feature vectors capturing scene-level representations
 - **Patch Tokens**: 256×768 arrays (16×16 spatial grid) encoding local features
 - **Training**: Self-supervised learning on natural images
 
 ### DINOv3 Large (`facebook/dinov3-vitl16-pretrain-sat493m`)
 - **Architecture**: Vision Transformer Large with 16×16 patch size
-- **CLS Tokens**: 1024-dimensional global feature vectors capturing scene-level semantics
+- **CLS Tokens**: 1024-dimensional global feature vectors capturing scene-level representations
 - **Patch Tokens**: 196×1024 arrays (14×14 spatial grid) encoding local features
 - **Training**: Self-supervised learning with satellite imagery pretraining
 
@@ -274,7 +142,7 @@ The dataset includes pre-computed embeddings from two state-of-the-art vision tr
 
 This dataset is intended for:
 - Developing vision encoders robust to lighting variations
-- Semantic stability research in computer vision
+- Representation stability research in computer vision
 - Time-invariant feature learning
 - Remote sensing applications requiring lighting robustness
 - Comparative analysis of illumination effects on vision model features
@@ -285,13 +153,13 @@ If you use this dataset in your research, please cite:
 
 ```bibtex
 @dataset{mpg_ranch_light_stable_semantics_2024,
-  title={Light Stable Semantics Dataset},
+  title={Light Stable Representations Dataset},
   author={Kyle Doherty and Erik Samose and Max Gurinas and Brandon Trabucco and Ruslan Salakhutdinov},
   year={2024},
   month={November},
-  url={https://huggingface.co/datasets/mpg-ranch/light-stable-semantics},
+  url={https://huggingface.co/datasets/mpg-ranch/drone-lsr},
   publisher={Hugging Face},
-  note={Aerial orthomosaic tiles with DINOv2 and DINOv3 embeddings for light-stable semantic vision encoder training},
+  note={Aerial orthomosaic tiles with DINOv2 and DINOv3 embeddings for light-stable representation vision encoder training},
   location={MPG Ranch, Montana, USA},
   survey_date={2024-11-07},
   organization={MPG Ranch}