Metadata-Version: 2.1
Name: autodistill-altclip
Version: 0.1.2
Summary: AltCLIP model for use with Autodistill.
Home-page: https://github.com/autodistill/autodistill-altclip
Author: Roboflow
Author-email: support@roboflow.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: transformers
Requires-Dist: torch
Requires-Dist: autodistill
Requires-Dist: supervision
Requires-Dist: numpy
Requires-Dist: Pillow
Provides-Extra: dev
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: black ==22.3.0 ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: twine ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: wheel ; extra == 'dev'

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# Autodistill AltCLIP Module

This repository contains the code supporting the AltCLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[AltCLIP](https://arxiv.org/abs/2211.06679v2) is a multi-modal vision model. With AltCLIP, you can compare the similarity between text and images, or the similarlity between two images. AltCLIP was trained on multi-lingual text-image pairs, which means it can be used for zero-shot classification with text prompts in different languages. [Read the AltCLIP paper for more information](https://arxiv.org/pdf/2211.06679v2.pdf).

The Autodistill AltCLIP module enables you to use AltCLIP for zero-shot 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 AltCLIP with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-altclip
```

## Quickstart

```python
from autodistill_altclip import AltCLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our AltCLIP 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 results
# then, load the model
base_model = AltCLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

results = base_model.predict("construction.jpg")

print(results)
```

## License

The AltCLIP model is licensed under an [Apache 2.0 license](LICENSE). See the [model README](https://github.com/FlagAI-Open/FlagAI/blob/master/examples/AltCLIP/README.md) for more information.

## 🏆 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!
