https://github.com/autodistill/autodistill-bioclip
BioCLIP base model for use with Autodistill.
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.2%) to scientific vocabulary
Keywords
Repository
BioCLIP base model for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://docs.autodistill.com
- Size: 6.84 KB
Statistics
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
Autodistill BioCLIP Module
This repository contains the code supporting the BioCLIP base model for use with Autodistill.
BioCLIP is a CLIP model trained on the TreeOfLife-10M dataset, created by the researchers who made BioCLIP. The dataset on which BioCLIP was trained included more than 450,000 classes.
You can use BioCLIP to auto-label natural organisms (i.e. animals, plants) in images for use in training a classification model. You can combine this model with a grounded detection model to identify the exact region in which a given class is present in an image. Learn more about combining models with Autodistill.
Read the full Autodistill documentation.
Read the BioCLIP Autodistill documentation.
Installation
To use BioCLIP with autodistill, you need to install the following dependency:
bash
pip3 install autodistill-bioclip
Quickstart
```python from autodistill_bioclip import BioCLIP
define an ontology to map class names to our BioCLIP prompt
the ontology dictionary has the format {caption: class}
where caption is the prompt sent to the base model, and class is the label that will
be saved for that caption in the generated annotations
then, load the model
classes = ["arabica", "robusta"]
base_model = BioCLIP( ontology=CaptionOntology( { item: item for item in classes } ) )
results = base_model.predict("../arabica.jpeg")
top = results.gettopk(1) top_class = classes[top[0][0]]
print(f"Predicted class: {top_class}") ```
License
This project is licensed under an MIT license.
The underlying BioCLIP model is also licensed under an MIT license.
🏆 Contributing
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!
Owner
- Name: Autodistill
- Login: autodistill
- Kind: organization
- Email: autodistill@roboflow.com
- Website: https://autodistill.com
- Repositories: 1
- Profile: https://github.com/autodistill
Use bigger slower models to train smaller faster ones
GitHub Events
Total
Last Year
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| James Gallagher | j****g@j****g | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 1
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- hlapp (1)