Update README.md

README.md

@@ -45,7 +45,7 @@ pipeline_tag: zero-shot-image-classification
 </div>
 
 
-BIOTROVE is a new suite of vision-language foundation models for biodiversity. These CLIP-style foundation models were trained on [ARBORETUM-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.
+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
@@ -57,18 +57,12 @@ These models were developed for the benefit of the AI community as an open-sourc
 ### Model Description
 
 BioTrove 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: 
+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.
 
-
-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 - 
-- **BIOTROVE-O:** - `BIOTROVE-vit-b-16-from-openai-epoch-40.pt`,
-- **BIOTROVE-B:** - `BIOTROVE-vit-b-16-from-bioclip-epoch-8.pt`
-- **BIOTROVE-M** - `BIOTROVE-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 BioTrove models in zero-shot  image classification tasks.**
 
@@ -133,7 +127,7 @@ All the `BioTrove` models were evaluated on the challenging [CONFOUNDING-SPECIES
 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 [ARBORETUM-40M](https://baskargroup.github.io/Arboretum/) and
+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.