Update Readme to move download instructions at the top

README.md

@@ -49,54 +49,35 @@ Image with caption:
 -->
 
 # Dataset Card for Fish-Visual Trait Analysis (Fish-Vista)
-
-## Dataset Details
-
-### Dataset Description
-
-<!--
-- **Curated by:** list curators (authors for _data_ citation, moved up)
-- **Language(s) (NLP):** [More Information Needed]
-<!-- Provide the basic links for the dataset. These will show up on the sidebar to the right of your dataset card ("Curated by" too). -->
-<!--
-- **Homepage:** 
-- **Repository:** [related project repo]
-- **Paper:** 
--->
-
-<!-- Provide a longer summary of what this dataset is. -->
-
-The Fish-Visual Trait Analysis (Fish-Vista) dataset is a large, annotated collection of 60K fish images spanning 1900 different species; it supports several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks.
-
-The Fish Vista dataset consists of museum fish images from [Great Lakes Invasives Network (GLIN)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), and [Morphbank](https://www.morphbank.net/) databases. We acquired these images, along with associated metadata including the scientific species names, the taxonomical family the species belong to, and licensing information, from the [Fish-AIR repository](https://fishair.org/). 
-
 |![Figure 1](https://huggingface.co/datasets/imageomics/fish-vista/resolve/main/metadata/figures/FishVista.png)|
 |:--|
 |**Figure 1.** A schematic representation of the different tasks in Fish-Vista Dataset. |
 
-<!--This dataset card has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1), and further altered to suit Imageomics Institute needs.-->
-
-
-### Supported Tasks and Leaderboards
-<!--[Add some more description. could replace graphs with tables]-->
-
-|![Figure 2](https://huggingface.co/datasets/imageomics/fish-vista/resolve/main/metadata/figures/clf_imbalance.png)|
-|:--|
-|**Figure 2.** Comparison of the fine-grained classification performance of different imbalanced classification methods. |
-
-|![Figure 3](https://huggingface.co/datasets/imageomics/fish-vista/resolve/main/metadata/figures/IdentificationOriginalResults.png)|
-|:--|
-|**Figure 3.** Trait identification performance of different multi-label classification methods. |
-
-
-<!---
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
---->
-
+## Instructions for downloading dataset and images
+<!-- [Add instructions for downloading images here]
+-->
+* Install [Git LFS](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)
+* Git clone the fish-vista repository
+  * Run the following commands in a **terminal**:
+```bash
+git clone https://huggingface.co/datasets/imageomics/fish-vista
+cd fish-vista
+```
+* Run the following commands to move all chunked images to a single directory:
+```bash
+mkdir AllImages
+find Images -type f -exec mv -v {} AllImages \;
+rm -rf Images
+mv AllImages Images
+```
 
-### Languages
+  * You should now have all the images in the *Images* directory
 
-English
+* Run the following commands to download and process copyrighted images 
+```bash
+python download_and_process_nd_images.py --save_dir Images
+```
+  * This will download and process the CC-BY-ND images that we do not provide in the *Images* folder
 
 ## Dataset Structure
 
@@ -142,31 +123,6 @@ English
         data-bib.bib
 ```
 
-### Instructions for downloading dataset
-<!-- [Add instructions for downloading images here]
--->
-* Install [Git LFS](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)
-* Git clone the fish-vista repository
-  * Run the following commands in a **terminal**:
-```bash
-git clone https://huggingface.co/datasets/imageomics/fish-vista
-cd fish-vista
-```
-* Run the following commands to move all chunked images to a single directory:
-```bash
-mkdir AllImages
-find Images -type f -exec mv -v {} AllImages \;
-rm -rf Images
-mv AllImages Images
-```
-
-  * You should now have all the images in the *Images* directory
-
-* Run the following commands to download and process copyrighted images 
-```bash
-python download_and_process_nd_images.py --save_dir Images
-```
-  * This will download and process the CC-BY-ND images that we do not provide in the *Images* folder
 
 ### Data Instances
 
@@ -218,13 +174,57 @@ CSV Columns are as follows:
 - `barbel`: Presence/absence of the barbel trait. NA for the classification (FV-419) dataset, since it is only used for identification. 1 indicates presence and 0 indicates absence. This is used for trait identification.
 - `multiple_dorsal_fin`: Presence/absence of the dorsal fin trait. NA for the classification (FV-419) dataset, since it is only used for identification. 1 indicates presence, 0 indicates absence and -1 indicates unknown. This is used for trait identification.
 
-**Note:**
 
 
 ### Data Splits
 
 For each task (or subset), the split is indicated by the CSV name (e.g., `classification_<split>.csv`). More information is provided in [Data Instances](#data-instances), above.
-      
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!--
+- **Curated by:** list curators (authors for _data_ citation, moved up)
+- **Language(s) (NLP):** [More Information Needed]
+<!-- Provide the basic links for the dataset. These will show up on the sidebar to the right of your dataset card ("Curated by" too). -->
+<!--
+- **Homepage:** 
+- **Repository:** [related project repo]
+- **Paper:** 
+-->
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+The Fish-Visual Trait Analysis (Fish-Vista) dataset is a large, annotated collection of 60K fish images spanning 1900 different species; it supports several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks.
+
+The Fish Vista dataset consists of museum fish images from [Great Lakes Invasives Network (GLIN)](https://greatlakesinvasives.org/portal/index.php), [iDigBio](https://www.idigbio.org/), and [Morphbank](https://www.morphbank.net/) databases. We acquired these images, along with associated metadata including the scientific species names, the taxonomical family the species belong to, and licensing information, from the [Fish-AIR repository](https://fishair.org/). 
+
+
+<!--This dataset card has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1), and further altered to suit Imageomics Institute needs.-->
+
+
+### Supported Tasks and Leaderboards
+<!--[Add some more description. could replace graphs with tables]-->
+
+|![Figure 2](https://huggingface.co/datasets/imageomics/fish-vista/resolve/main/metadata/figures/clf_imbalance.png)|
+|:--|
+|**Figure 2.** Comparison of the fine-grained classification performance of different imbalanced classification methods. |
+
+|![Figure 3](https://huggingface.co/datasets/imageomics/fish-vista/resolve/main/metadata/figures/IdentificationOriginalResults.png)|
+|:--|
+|**Figure 3.** Trait identification performance of different multi-label classification methods. |
+
+
+<!---
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+--->
+
+### Languages
+
+English
+
 ## Dataset Creation
 
 ### Curation Rationale