added readme

README.md

@@ -2,65 +2,63 @@
 License: cc0-1.0
 language:
 - en
-pretty_name: Arboretum
+pretty_name: BioTrove
 task_categories:
-  - image-classification
-  - zero-shot-classification
+- image-classification
+- zero-shot-classification
 tags:
-  - biology
-  - image
-  - animals
-  - species
-  - taxonomy
-  - rare species
-  - endangered species
-  - evolutionary biology
-  - balanced
-  - CV
-  - multimodal
-  - CLIP
-  - knowledge-guided
-
+- biology
+- image
+- animals
+- species
+- taxonomy
+- rare species
+- endangered species
+- evolutionary biology
+- balanced
+- CV
+- multimodal
+- CLIP
+- knowledge-guided
 size_categories: 100M<n<1B
-
 ---
 
-# Arboretum: A Comprehensive Multimodal Dataset Enabling AI for Biodiversity
+# BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
 
 <!-- Banner links -->
 <div style="text-align:center;">
-  <a href="https://baskargroup.github.io/Arboretum/" target="_blank">
+  <a href="https://baskargroup.github.io/BioTrove/ target="_blank">
     <img src="https://img.shields.io/badge/Project%20Page-Visit-blue" alt="Project Page" style="margin-right:10px;">
   </a>
-  <a href="https://github.com/baskargroup/Arboretum" target="_blank">
+  <a href="https://github.com/baskargroup/BioTrove" target="_blank">
     <img src="https://img.shields.io/badge/GitHub-Visit-lightgrey" alt="GitHub" style="margin-right:10px;">
   </a>
   <a href="https://pypi.org/project/arbor-process/" target="_blank">
-    <img src="https://img.shields.io/badge/PyPI-arbor--process%200.1.0-orange" alt="PyPI arbor-process 0.1.0">
+    <img src="https://img.shields.io/badge/PyPI-arbor--process%200.1.0-orange" alt="PyPI biotrove-process 0.1.0">
   </a>
 </div>
 
 ## Description
 
-[Arboretum](https://baskargroup.github.io/Arboretum/) comprises well-processed metadata with full taxa information and URLs pointing to image files. The metadata can be used to filter specific categories, visualize data distribution, and manage imbalance effectively. We provide a collection of software tools that enable users to easily download, access, and manipulate the dataset. 
+[BioTrove](https://baskargroup.github.io/BioTrove/) comprises well-processed metadata with full taxa information and URLs pointing to image files. The metadata can be used to filter specific categories, visualize data distribution, and manage imbalance effectively. We provide a collection of software tools that enable users to easily download, access, and manipulate the dataset. 
 
 
-## Arboretum Dataset
-`Arboretum` comprises over `134.6M` images across seven taxonomic classes —Aves, Arachnida, Insecta, Plantae, Fungi, Mollusca, and Reptilia. 
-These taxonomic classes were chosen to represent the span of species — outside of charismatic megafauna. The images in Arboretum span `326,888`species. 
-Overall, this dataset nearly matches the state-of-the-art curated dataset (TREEOFLIFE-10M) in terms of species diversity, while comfortably exceeding it in terms of scale by a factor of nearly 13.5 times.
+## BioTrove Dataset
+`BioTrove` comprises over `161.9M` images across several taxonomic groups- including Reptilia (reptiles), Plantae (plants), Mollusca (mollusks), Mammalia (mammals), Insecta (insects), Fungi (fungi), Aves (birds), Arachnida (arachnids), Animalia (animals), Amphibia (amphibians), and Actinopterygii (ray-finned fish).  
+These taxonomic groups were chosen to represent the span of species — outside of charismatic megafauna. The images in BioTrove span `366.6K`species. 
+Overall, this dataset nearly matches the state-of-the-art curated dataset (TREEOFLIFE-10M) in terms of species diversity, while comfortably exceeding it in terms of scale by a factor of nearly 16.2 times.
 
 ## New Benchmark Datasets
 We created three new benchmark datasets for fine-grained image classification. In addition, we provide a new benchmark dataset for species recognition across various developmental Life-stages.
 
-### Arboretum-Balanced
-For balanced species distribution across the 7 categories, we curated `Arboretum-Balanced`. Each category includes up to 500 species, with 50 images per species.
+### BioTrove-Balanced
+For balanced species distribution across the 7 categories, we curated `BioTrove-Balanced`. Each category includes up to 500 species, with 50 images per species.
 
-### Arboretum-Unseen
-To provide a robust benchmark for evaluating the generalization capability of models on unseen species, we curated `Arboretum-Unseen`. The test dataset was constructed by identifying species with fewer than 30 instances in ARBORETUM, ensuring that the dataset contains species that were unseen by ARBORCLIP. Each species contained 10 images.
+### BioTrove-Unseen
+To provide a robust benchmark for evaluating the generalization capability of models on unseen species, we curated `BioTrove-Unseen`. The test dataset was constructed by identifying species with fewer than 30 instances in BioTrove, ensuring that the dataset contains species that were unseen by BioTrove-CLIP. Each species contained 10 images.
 
-### Arboretum-LifeStages
-To assess the model’s ability to recognize species across various developmental stages, we curated `Arboretum-LifeStages`. This dataset has 20 labels in total and focuses on insects, since these species often exhibit significant visual differences across their lifespan. Arboretum-LifeStages contains five insect species and utilized the observation export feature on the iNaturalist platform to collect data from 2/1/2024 to 5/20/2024 to ensure no overlap with the training dataset. For each species, life stage filters (egg, larva, pupa, or adult) were applied.
+### BioTrove-LifeStages
+To assess the model’s ability to recognize species across various developmental stages, we curated `BioTrove-LifeStages`. This dataset has 20 labels in total and focuses on insects, since these species often exhibit significant visual differences across their lifespan. BioTrove-LifeStages contains five insect species and utilized the observation export feature on the iNaturalist platform to collect data from 2/1/2024 to 5/20/2024 to ensure no overlap with the training dataset. For each species, life stage filters (egg, larva, pupa, or adult) were applied.
 
 ## Dataset Information
 
@@ -72,42 +70,42 @@ To assess the model’s ability to recognize species across various developmenta
 - **Life Stages Dataset**: Focuses on insects across various developmental stages.
 
 
-## ArborCLIP Models
+## BioTrove-CLIP Models
 
-**See the [ArborCLIP](https://huggingface.co/ChihHsuan-Yang/ArborCLIP) model card on HuggingFace to download the trained model checkpoints**
+**See the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace to download the trained model checkpoints**
 
-We released three trained model checkpoints in the [ArborCLIP](https://huggingface.co/ChihHsuan-Yang/ArborCLIP) model card on HuggingFace. These CLIP-style models were trained on [ARBORETUM-40M](https://baskargroup.github.io/Arboretum/) for the following configurations: 
+We released three trained model checkpoints in the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace. These CLIP-style models were trained on [BioTrove-40M](https://baskargroup.github.io/BioTrove/) for the following configurations: 
 
-- **ARBORCLIP-O:** Trained a ViT-B/16 backbone initialized from the [OpenCLIP's](https://github.com/mlfoundations/open_clip) checkpoint. The training was conducted for 40 epochs.
-- **ARBORCLIP-B:** Trained a ViT-B/16 backbone initialized from the [BioCLIP's](https://github.com/Imageomics/BioCLIP) checkpoint. The training was conducted for 8 epochs.
-- **ARBORCLIP-M:** Trained a ViT-L/14 backbone initialized from the [MetaCLIP's](https://github.com/facebookresearch/MetaCLIP) checkpoint. The training was conducted for 12 epochs.
+- **BioTrove-CLIP-O:** Trained a ViT-B/16 backbone initialized from the [OpenCLIP's](https://github.com/mlfoundations/open_clip) checkpoint. The training was conducted for 40 epochs.
+- **BioTrove-CLIP-B:** Trained a ViT-B/16 backbone initialized from the [BioCLIP's](https://github.com/Imageomics/BioCLIP) checkpoint. The training was conducted for 8 epochs.
+- **BioTrove-CLIP-M:** Trained a ViT-L/14 backbone initialized from the [MetaCLIP's](https://github.com/facebookresearch/MetaCLIP) checkpoint. The training was conducted for 12 epochs.
 
 
 
 ## Usage
 
-**To start using the Arboretum dataset, follow the instructions provided in the [GitHub](https://github.com/baskargroup/Arboretum). Model checkpoints are shared in the [model_ckpt](#directory) directory.**
+**To start using the BioTrove dataset, follow the instructions provided in the [GitHub](https://github.com/baskargroup/BioTrove). Model checkpoints are shared in the [model_ckpt](#directory) directory.**
 
-**Metadata files are included in the [Directory](#directory). Please download the metadata from the [Directory](#directory)** and pre-process the data using the [arbor_process](https://pypi.org/project/arbor-process/) PyPI library. The instructions to use the library can be found in [here](https://github.com/baskargroup/Arboretum/blob/main/Arbor-preprocess/README_arbor_process.md). The Readme file contains the detailed description of data preparation steps.
+**Metadata files are included in the [Directory](#directory). Please download the metadata from the [Directory](#directory)** and pre-process the data using the [arbor_process](https://pypi.org/project/arbor-process/) PyPI library. The instructions to use the library can be found in [here](https://github.com/baskargroup/BioTrove/blob/main/Arbor-preprocess/README_arbor_process.md). The Readme file contains the detailed description of data preparation steps.
 
 ### Directory
 ```plaintext
 main/
-├── Arboretum/
+├── BioTrove/
 │   ├── chunk_0.csv
 │   ├── chunk_0.parquet
 │   ├── chunk_1.parquet
 │   ├── .
 │   ├── .
 │   ├── .
-│   └── chunk_2691.parquet
-├── Arboretum-benchmark/
-│   ├── Arboretum-Balanced.csv
-│   ├── Arboretum-Balanced.parquet
-│   ├── Arboretum-Lifestages.csv
-│   ├── Arboretum-Lifestages.parquet
-│   ├── Arboretum-Unseen.csv
-│   └──Arboretum-Unseen.parquet
+│   └── chunk_3251.parquet
+├── BioTrove-benchmark/
+│   ├── BioTrove-Balanced.csv
+│   ├── BioTrove-Balanced.parquet
+│   ├── BioTrove-Lifestages.csv
+│   ├── BioTrove-Lifestages.parquet
+│   ├── BioTrove-Unseen.csv
+│   └──BioTrove-Unseen.parquet
 ├──README.md
 └──.gitignore
 ```
@@ -124,8 +122,8 @@ expertise.
   <div class="container is-max-widescreen content">
       <h2 class="title">Citation</h2>
       If you find this dataset useful in your research, please consider citing our paper:
-      <pre><code>@misc{yang2024arboretumlargemultimodaldataset,
-        title={Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity}, 
+      <pre><code>@misc{yang2024BioTrovelargemultimodaldataset,
+        title={BioTrove: A Large Multimodal Dataset Enabling AI for Biodiversity}, 
         author={Chih-Hsuan Yang, Benjamin Feuer, Zaki Jubery, Zi K. Deng, Andre Nakkab,
            Md Zahid Hasan, Shivani Chiranjeevi, Kelly Marshall, Nirmal Baishnab, Asheesh K Singh,
             Arti Singh, Soumik Sarkar, Nirav Merchant, Chinmay Hegde, Baskar Ganapathysubramanian},
@@ -141,4 +139,4 @@ expertise.
 
 ---
 
-For more details and access to the dataset, please visit the [Project Page](https://baskargroup.github.io/Arboretum/).
+For more details and access to the dataset, please visit the [Project Page](https://baskargroup.github.io/BioTrove/).