Zero-Shot Image Classification
OpenCLIP
English
clip
biology
CV
images
animals
species
taxonomy
rare species
endangered species
evolutionary biology
multimodal
knowledge-guided
Instructions to use imageomics/bioclip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use imageomics/bioclip with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:imageomics/bioclip') tokenizer = open_clip.get_tokenizer('hf-hub:imageomics/bioclip') - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesUpdate following Rare Species data adjustment, consistent with Rare Species dataset as published (https://doi.org/10.57967/hf/1511).
README.md
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BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology.
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It is trained on [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M), our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date.
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Through rigorous benchmarking on a diverse set of fine-grained biological classification tasks, BioCLIP consistently outperformed existing baselines by
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Through intrinsic evaluation, we found that BioCLIP learned a hierarchical representation aligned to the tree of life, which demonstrates its potential for robust generalizability.
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**See the `examples/` directory for examples of how to use BioCLIP in zero-shot and few-shot settings.**
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BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology.
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It is trained on [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M), our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date.
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Through rigorous benchmarking on a diverse set of fine-grained biological classification tasks, BioCLIP consistently outperformed existing baselines by 16% to 17% absolute.
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Through intrinsic evaluation, we found that BioCLIP learned a hierarchical representation aligned to the tree of life, which demonstrates its potential for robust generalizability.
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**See the `examples/` directory for examples of how to use BioCLIP in zero-shot and few-shot settings.**
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