Update README.md

README.md

@@ -61,15 +61,15 @@ dataset_info:
         sequence: int64
 ---
 
-### Nature Multi-View (NMV) Dataset Datacard
+# Nature Multi-View (NMV) Dataset Datacard
+To encourage development of better machine learning methods for operating with diverse, unlabeled natural world imagery, we introduce Nature Multi-View (NMV), a multi-view dataset of over 3 million ground-level and aerial image pairs from over 1.75 million citizen science observations for over 6,000 native and introduced plant species across California.
 
-
-# Dataset Overview
+## Dataset Overview
 - Total Images: Over 3 million ground-level and aerial image pairs
 - Species Covered: Over 6,000 native and introduced plant species
 - Geographic Focus: California, USA
 
-# Data Description
+## Data Description
 - Ground-Level Images:
   - Sourced from iNaturalist open data on AWS.
   - Filters applied:
@@ -85,7 +85,7 @@ dataset_info:
   - RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution.
   - Centered on the latitude and longitude of the iNaturalist observation.
   
-# Dataset Splits
+## Dataset Splits
 - Training Set:
   - Full Training Set: 1,755,602 observations, 3,307,025 images
   - Labeled Training Sets:
@@ -98,11 +98,11 @@ dataset_info:
 - Test Set:
   - 182,618 observations, 334,887 images
 
-# Characteristics and Challenges
+## Characteristics and Challenges
 - Long-Tail Distribution: The dataset exhibits a long-tail distribution common in natural world settings, making it a realistic benchmark for machine learning applications.
 - Geographic Bias: Reflects the geographic bias of citizen science data, with more observations from densely populated and visited regions like urban areas and National Parks.
 - Many-to-One Pairing: Multiple ground-level images are paired to the same aerial image, which may require special handling in machine learning models.
 
-# References
+## References
 - iNaturalist: www.inaturalist.org
 - nited States Department of Agriculture: NAIP Imagery.