Update README.md

README.md

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         sequence: int64
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-Nature Multi-View (NMV) Dataset Datacard
-Dataset Overview
+### Nature Multi-View (NMV) Dataset Datacard
 
-Name: Nature Multi-View Dataset (NMV)
-Total Observations: 3,921,026
-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
 
-Ground-Level Images:
-Sourced from iNaturalist open data on AWS.
-Filters applied:
-Vascular plants
-Within California state boundaries
-Observations dated from January 1, 2011, to September 27, 2023
-Geographic uncertainty < 120 meters
-Research-grade or in need of ID (excluding casual observations)
-Availability of corresponding remote sensing imagery
-Overlap with bio-climatic variables
-Aerial Images:
-Sourced from the 2018 National Agriculture Imagery Program (NAIP).
-RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution.
-Centered on the latitude and longitude of the iNaturalist observation.
-Dataset Splits
+# 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
 
-Training Set:
-Full Training Set: 1,755,602 observations, 3,307,025 images
-Labeled Training Sets:
-20%: 334,383 observations, 390,908 images
-5%: 93,708 observations, 97,727 images
-1%: 19,371 observations, 19,545 images
-0.25%: 4,878 observations, 4,886 images
-Validation Set:
-150,555 observations, 279,114 images
-Test Set:
-182,618 observations, 334,887 images
-Characteristics and Challenges
+# Data Description
+- Ground-Level Images:
+  - Sourced from iNaturalist open data on AWS.
+  - Filters applied:
+  - Vascular plants
+  - Within California state boundaries
+  - Observations dated from January 1, 2011, to September 27, 2023
+  - Geographic uncertainty < 120 meters
+  - Research-grade or in need of ID (excluding casual observations)
+  - Availability of corresponding remote sensing imagery
+  - Overlap with bio-climatic variables
+- Aerial Images:
+  - Sourced from the 2018 National Agriculture Imagery Program (NAIP).
+  - RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution.
+  - Centered on the latitude and longitude of the iNaturalist observation.
+  
+# Dataset Splits
+- Training Set:
+  - Full Training Set: 1,755,602 observations, 3,307,025 images
+  - Labeled Training Sets:
+    - 20%: 334,383 observations, 390,908 images
+    - 5%: 93,708 observations, 97,727 images
+    - 1%: 19,371 observations, 19,545 images
+    - 0.25%: 4,878 observations, 4,886 images
+- Validation Set:
+  - 150,555 observations, 279,114 images
+- Test Set:
+  - 182,618 observations, 334,887 images
 
-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.
-Self-Supervised Learning: Designed to encourage the development of self-supervised machine learning methods, unrestricted by label balance.
+# 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
+- iNaturalist: www.inaturalist.org
+- nited States Department of Agriculture: NAIP Imagery.