https://github.com/autodistill/autodistill-llava
LLaVA 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
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.5%) to scientific vocabulary
Keywords
Repository
LLaVA base model for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://docs.autodistill.com
- Size: 17.6 KB
Statistics
- Stars: 6
- Watchers: 4
- Forks: 4
- Open Issues: 8
- Releases: 0
Topics
Metadata Files
README.md
Autodistill LLaVA Module
This repository contains the code supporting the LLaVA base model for use with Autodistill.
LLaVA is a multi-modal language model with object detection capabilities. You can use LLaVA with autodistill for object detection. Learn more about LLaVA 1.5, the most recent version of LLaVA at the time of releasing this package.
Read the full Autodistill documentation.
Read the LLaVA Autodistill documentation.
Installation
To use LLaVA with autodistill, you need to install the following dependency:
bash
pip3 install autodistill-llava
Quickstart
```python from autodistill_llava import LLaVA
define an ontology to map class names to our LLaVA 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
basemodel = LLaVA( ontology=CaptionOntology( { "a forklift": "forklift" } ) ) basemodel.label("./context_images", extension=".jpeg") ```
License
This model is licensed under an Apache 2.0 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
- Watch event: 2
- Fork event: 2
Last Year
- Watch event: 2
- Fork event: 2
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 13
- Total pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: 3 minutes
- Total issue authors: 6
- Total pull request authors: 1
- Average comments per issue: 1.77
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 8
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 3
- Pull request authors: 0
- Average comments per issue: 2.5
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- shashi-netra (2)
- shersoni610 (2)
- whatisslove11 (1)
- Samuel5106 (1)
- egnerfl (1)
- csv610 (1)
Pull Request Authors
- 0asa (2)