updated readme

README.md

@@ -6,6 +6,8 @@ tags:
   - zero-shot-image-classification
   - clip
   - biology
+  - biodiversity
+  - agronomy
   - CV
   - images
   - animals
@@ -17,6 +19,7 @@ tags:
   - multimodal
   - knowledge-guided
 datasets:
+  - Arboretum
   - imageomics/TreeOfLife-10M
   - iNat21
   - BIOSCAN-1M
@@ -46,7 +49,7 @@ ARBORCLIP is a new suite of vision-language foundation models for biodiversity.
 - **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.
+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
@@ -54,12 +57,15 @@ These models were developed for the benefit of the AI community as an open-sourc
 ArborCLIP is based on OpenAI's [CLIP](https://openai.com/research/clip) model. 
 The models were trained on [ARBORETUM-40M](https://baskargroup.github.io/Arboretum/) for the following configurations: 
 
-- **ARBORCLIP-O:** Trained a ViT-B/16 backbone initialized from the [OpenCLIP's](https://github.com/mlfoundations/open_clip) checkpoint. 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. 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. Training was conducted for 12 epochs.
+- **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.
 
 
-To access the checkpoints of the above models, go to the `Files and versions` tab and download the weights. These weights can be directly used for zero-shot classification and finetuning. The filenames correspond to the specific model weights - `arborclip-vit-b-16-from-openai-epoch-40.pt` (**ARBORCLIP-O**), `arborclip-vit-b-16-from-bioclip-epoch-8.pt` (**ARBORCLIP-B**) and`arborclip-vit-l-14-from-metaclip-epoch-12.pt` (**ARBORCLIP-M**).
+To access the checkpoints of the above models, go to the `Files and versions` tab and download the weights. These weights can be directly used for zero-shot classification and finetuning. The filenames correspond to the specific model weights - 
+- **ARBORCLIP-O:** - `arborclip-vit-b-16-from-openai-epoch-40.pt`,
+- **ARBORCLIP-B:** - `arborclip-vit-b-16-from-bioclip-epoch-8.pt`
+- **ARBORCLIP-M** - `arborclip-vit-l-14-from-metaclip-epoch-12.pt`
 
 ### 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 ArborCLIP models in zero-shot  image classification tasks.**
@@ -128,8 +134,7 @@ Additionally, models trained on web-scraped data excel at classifying at the hig
 level (kingdom), while models begin to benefit from specialist datasets like [ARBORETUM-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, `ArborCLIP` 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 `ArborCLIP` in
-real-world biodiversity monitoring tasks.
+Addressing these limitations will further enhance the applicability of models like `ArborCLIP` 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