BioCLIP
| Property | Value |
|---|---|
| Model Type | Vision Transformer (ViT-B/16) |
| License | MIT |
| Base Model | OpenAI CLIP |
| Paper | BioCLIP: A Vision Foundation Model for the Tree of Life (arXiv) |
What is BioCLIP?
BioCLIP is a groundbreaking foundation model designed specifically for biological classification across the tree of life. Built on the CLIP architecture, it has been trained on TreeOfLife-10M, an extensive dataset encompassing over 450,000 taxa, making it the most biologically diverse machine learning dataset available. The model demonstrates exceptional capability in understanding hierarchical relationships between species, significantly outperforming existing models by 16-17% in fine-grained biological classification tasks.
Implementation Details
BioCLIP is implemented using the Vision Transformer (ViT-B/16) architecture and trained using OpenCLIP's codebase. The training process involved 8 NVIDIA A100-80GB GPUs, with a global batch size of 32,768, running for 4 days on OSC's Ascend HPC Cluster. The model processes images at 224x224 pixels resolution and employs mixed precision training with carefully tuned hyperparameters.
- Trained on TreeOfLife-10M dataset with taxonomic hierarchy integration
- Uses fp16 mixed precision training
- Implements cosine decay learning rate scheduling
- Supports both zero-shot and few-shot classification
Core Capabilities
- Zero-shot species classification across diverse biological domains
- Hierarchical representation learning aligned with taxonomic structure
- Superior performance on specialized biological datasets (Birds 525, Plankton, Insects, etc.)
- Robust generalization across different biological classification tasks
Frequently Asked Questions
Q: What makes this model unique?
BioCLIP's unique strength lies in its ability to understand and represent the hierarchical relationships in the tree of life, rather than treating species as isolated categories. This enables better generalization and more nuanced biological classification capabilities.
Q: What are the recommended use cases?
The model is recommended for biological computer vision tasks, particularly species classification and identification. It can be used in both zero-shot and few-shot settings, making it valuable for researchers working with limited data or exploring new species classifications.
