<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 CLIP Module
This repository contains the code supporting the CLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[CLIP](https://github.com/openai/CLIP), developed by OpenAI, is a computer vision model trained using pairs of images and text. You can use CLIP with autodistill for image classification.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [CLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/clip/).
## Installation
To use CLIP with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-clip
```
## Quickstart
```python
from autodistill_clip import CLIP
from autodistill.detection import CaptionOntology
# define an ontology to map class names to our CLIP 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 = CLIP(
ontology=CaptionOntology(
{
"person": "person",
"a forklift": "forklift"
}
)
)
results = base_model.predict("./context_images/test.jpg")
print(results)
base_model.label("./context_images", extension=".jpeg")
```
## License
The code in this repository is licensed under an [MIT license](LICENSE.md).
## 🏆 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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