imageomics/bioclip-2

Zero-Shot Image Classification • Updated May 20 • 85.8k • 33

Note BioCLIP 2 is a foundation model for biology organismal images. It is trained on TreeOfLife-200M on the basis of a CLIP model (ViT-14/L) pre-trained on LAION-2B. BioCLIP 2 yields state-of-the-art performance in recognizing various species. More importantly, it demonstrates emergent properties beyond species classification after extensive hierarchical contrastive training.

imageomics/bioclip-2.5-vith14

Zero-Shot Image Classification • Updated Feb 13 • 35.1k • 13

Note Foundation model for biology organismal images. It is trained on an updated version of TreeOfLife-200M on the basis of a CLIP model (ViT-H/14) pre-trained on LAION-2B. BioCLIP 2.5 Huge yields state-of-the-art performance in recognizing various species and further improves the performance on broader biological visual tasks. As a much larger model, it has increased inference resource needs over BioCLIP 2.

imageomics/biocap

Zero-Shot Image Classification • Updated Oct 24, 2025 • 224 • 1

Note Foundation model for biology organismal images. It is trained on TreeOfLife-10M with synthetic captions (TreeOfLife-10M-Captions) as supervision on the basis of a CLIP model (ViT-B/16) pre-trained by OpenAI. BioCAP achieves state-of-the-art performance on text-image retrieval tasks.

imageomics/bioclip

Zero-Shot Image Classification • Updated Feb 26 • 30.9k • 62

Note BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology. It is trained on TreeOfLife-10M, our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date.

Note With nearly 214 million images representing 952,257 taxa across the tree of life, TreeOfLife-200M is the largest and most diverse public ML-ready dataset for computer vision models in biology at release. This dataset combines images and metadata from four core biodiversity data providers: Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EOL), BIOSCAN-5M, and FathomNet to more than double the number of unique taxa covered by TreeOfLife-10M.

Note Over 10 million images covering 454 thousand taxa in the tree of life used to train BioCLIP. This dataset of images of biological organisms paired with their associated taxonomic labels expands on the foundation established by existing high-quality datasets, such as iNat21 and BIOSCAN-1M, by further incorporating newly curated images from the Encyclopedia of Life (eol.org), which supplies most of TreeOfLife-10M’s data diversity.

Note This dataset was generated alongside TreeOfLife-10M as a benchmark for BioCLIP; data (images and text) were pulled from Encyclopedia of Life (EOL) to generate a dataset consisting of rare species for zero-shot-classification and more refined image classification tasks. Here, we use "rare species" to mean species listed on The International Union for Conservation of Nature (IUCN) Red List as Near Threatened, Vulnerable, Endangered, Critically Endangered, and Extinct in the Wild.

Note IDLE-OO Camera Traps is a 5-dataset benchmark of camera trap images from the Labeled Information Library of Alexandria: Biology and Conservation (LILA BC) with a total of 2,586 images for species classification. Each of the 5 benchmarks is balanced to have the same number of images for each species within it (between 310 and 1120 images), representing between 16 and 39 species.

Note This dataset consists of generated captions, Wikipedia-derived descriptions, and format examples for the TreeOfLife-10M. These captions were generated using InternVL3-38B based on biological contexts that help the model generate more accurate captions. It was used to train BioCAP, a CLIP-based model.

imageomics/bioclip-vit-b-16-inat-only

Zero-Shot Image Classification • Updated Nov 18, 2025 • 338 • 2

Note This model is trained on iNat21, different from BioCLIP which is trained on TreeOfLife-10M.

Note This repo contains the real-time exploratory analysis of the TreeOfLife10M and Rare Species datasets as they were being crafted and refined.