| Name | autodistill-llava JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | LLaVA for use with Autodistill |
| upload_time | 2023-10-16 13:05:00 |
| maintainer | |
| docs_url | None |
| author | Roboflow |
| requires_python | >=3.7 |
| license | |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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<div align="center">
<p>
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
>
</a>
</p>
</div>
# Autodistill LLaVA Module
This repository contains the code supporting the LLaVA base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[LLaVA](https://github.com/haotian-liu/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](https://blog.roboflow.com/first-impressions-with-llava-1-5/), the most recent version of LLaVA at the time of releasing this package.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [LLaVA Autodistill documentation](https://autodistill.github.io/autodistill/base_models/llava/).
## Installation
To use CLIP with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-clip
```
## 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
base_model = LLaVA(
ontology=CaptionOntology(
{
"a forklift": "forklift"
}
)
)
base_model.label("./context_images", extension=".jpeg")
```
## License
This model is licensed under an [Apache 2.0 License](LICENSE).
## 🏆 Contributing
We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
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