---
license: mit
language:
- en
tags:
- zero-shot-image-classification
- OpenCLIP
- clip
- biology
- biodiversity
- agronomy
- CV
- images
- animals
- species
- taxonomy
- rare species
- endangered species
- evolutionary biology
- multimodal
- knowledge-guided
datasets:
- ChihHsuan-Yang/Arboretum
- EOL
base_model:
- openai/clip-vit-base-patch16
- openai/clip-vit-large-patch14
pipeline_tag: zero-shot-image-classification
---
# Model Card for BioTrove
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<a href="https://baskargroup.github.io/Arboretum/" target="_blank">
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BIOTROVE is a new suite of vision-language foundation models for biodiversity. These CLIP-style foundation models were trained on [BIOTROVE-40M](https://baskargroup.github.io/Arboretum/), which is a large-scale dataset of 40 million images of 33K species of plants and animals. The models are evaluated on zero-shot image classification tasks.
- **Model type:** Vision Transformer (ViT-B/16, ViT-L/14)
- **License:** MIT
- **Fine-tuned from model:** [OpenAI CLIP](https://github.com/mlfoundations/open_clip), [MetaCLIP](https://github.com/facebookresearch/MetaCLIP), [BioCLIP](https://github.com/Imageomics/BioCLIP)
These models were developed for the benefit of the AI community as an open-source product. Thus, we request that any derivative products are also open-source.
### Model Description
BioTrove is based on OpenAI's [CLIP](https://openai.com/research/clip) model.
The models were trained on [BIOTROVE-40M](https://baskargroup.github.io/Arboretum/) for the following configurations:
- **BIOTROVE-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-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-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.
### Model Training
**See the [Model Training](https://github.com/baskargroup/Arboretum?tab=readme-ov-file#model-training) section on the [Github](https://github.com/baskargroup/Arboretum) for examples of how to use BioTrove models in zero-shot image classification tasks.**
We train three models using a modified version of the [BioCLIP / OpenCLIP](https://github.com/Imageomics/bioclip/tree/main/src/training) codebase. Each model is trained on BioTrove-40M, on 2 nodes, 8xH100 GPUs, on NYU's [Greene](https://sites.google.com/nyu.edu/nyu-hpc/hpc-systems/greene) high-performance compute cluster. We publicly release all code needed to reproduce our results on the [Github](https://github.com/baskargroup/Arboretum) page.
We optimize our hyperparameters prior to training with [Ray](https://docs.ray.io/en/latest/index.html). Our standard training parameters are as follows:
```
--dataset-type webdataset
--pretrained openai
--text_type random
--dataset-resampled
--warmup 5000
--batch-size 4096
--accum-freq 1
--epochs 40
--workers 8
--model ViT-B-16
--lr 0.0005
--wd 0.0004
--precision bf16
--beta1 0.98
--beta2 0.99
--eps 1.0e-6
--local-loss
--gather-with-grad
--ddp-static-graph
--grad-checkpointing
```
For more extensive documentation of the training process and the significance of each hyperparameter, we recommend referencing the [OpenCLIP](https://github.com/mlfoundations/open_clip) and [BioCLIP](https://github.com/Imageomics/BioCLIP) documentation, respectively.
### Model Validation
For validating the zero-shot accuracy of our trained models and comparing to other benchmarks, we use the [VLHub](https://github.com/penfever/vlhub) repository with some slight modifications.
#### Pre-Run
After cloning the [Github](https://github.com/baskargroup/Arboretum) repository and navigating to the `BioTrove/model_validation` directory, we recommend installing all the project requirements into a conda container; `pip install -r requirements.txt`. Also, before executing a command in VLHub, please add `BioTrove/model_validation/src` to your PYTHONPATH.
```bash
export PYTHONPATH="$PYTHONPATH:$PWD/src";
```
#### Base Command
A basic BioTrove model evaluation command can be launched as follows. This example would evaluate a CLIP-ResNet50 checkpoint whose weights resided at the path designated via the `--resume` flag on the ImageNet validation set, and would report the results to Weights and Biases.
```bash
python src/training/main.py --batch-size=32 --workers=8 --imagenet-val "/imagenet/val/" --model="resnet50" --zeroshot-frequency=1 --image-size=224 --resume "/PATH/TO/WEIGHTS.pth" --report-to wandb
```
### Training Dataset
- **Dataset Repository:** [BioTrove](https://github.com/baskargroup/Arboretum)
- **Dataset Paper:** BioTrove: A Large Multimodal Dataset Enabling AI for Biodiversity ([arXiv](https://arxiv.org/abs/2406.17720))
- **HF Dataset card:** [BioTrove](https://huggingface.co/datasets/ChihHsuan-Yang/Arboretum)
### Model's Limitation
All the `BioTrove` models were evaluated on the challenging [CONFOUNDING-SPECIES](https://arxiv.org/abs/2306.02507) benchmark. However, all the models performed at or below random chance. This could be an interesting avenue for follow-up work and further expand the models capabilities.
In general, we found that models trained on web-scraped data performed better with common
names, whereas models trained on specialist datasets performed better when using scientific names.
Additionally, models trained on web-scraped data excel at classifying at the highest taxonomic
level (kingdom), while models begin to benefit from specialist datasets like [BIOTROVE-40M](https://baskargroup.github.io/Arboretum/) and
[Tree-of-Life-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) at the lower taxonomic levels (order and species). From a practical standpoint, `BioTrove` is highly accurate at the species level, and higher-level taxa can be deterministically derived from lower ones.
Addressing these limitations will further enhance the applicability of models like `BioTrove` in real-world biodiversity monitoring tasks.
### Acknowledgements
This work was supported by the AI Research Institutes program supported by the NSF and USDA-NIFA under [AI Institute: for Resilient Agriculture](https://aiira.iastate.edu/), Award No. 2021-67021-35329. This was also
partly supported by the NSF under CPS Frontier grant CNS-1954556. Also, we gratefully
acknowledge the support of NYU IT [High Performance Computing](https://www.nyu.edu/life/information-technology/research-computing-services/high-performance-computing.html) resources, services, and staff
expertise.
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-widescreen content">
<h2 class="title">Citation</h2>
If you find the models and datasets useful in your research, please consider citing our paper:
<pre><code>@misc{yang2024arboretumlargemultimodaldataset,
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},
year={2024},
eprint={2406.17720},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2406.17720},
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
---
For more details and access to the BioTrove dataset, please visit the [Project Page](https://baskargroup.github.io/Arboretum/).