| Name | autodistill-blip JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | |
| Summary | BLIP module for use with Autodistill |
| upload_time | 2023-12-06 10:42:47 |
| 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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<div align="center">
<p>
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png?4"
>
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# Autodistill BLIP Module
This repository contains the code supporting the BLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[BLIP](https://github.com/salesforce/LAVIS), developed by Salesforce, is a computer vision model that supports visual question answering and zero-shot classification. Autodistill supports classifying images using BLIP.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [BLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/blip/).
## Installation
To use BLIP with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-blip
```
## Quickstart
```python
from autodistill_blip import BLIP
# define an ontology to map class names to our BLIP 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 = BLIP(
ontology=CaptionOntology(
{
"person": "person",
"a forklift": "forklift"
}
)
)
base_model.label("./context_images", extension=".jpeg")
```
## License
This project is licensed under a [3-Clause BSD 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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