https://github.com/autodistill/autodistill-owlv2
OWLv2 base model for use with Autodistill.
https://github.com/autodistill/autodistill-owlv2
autodistill computer-vision object-detection owlv2 zero-shot-object-detection
Last synced: 2 months ago
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OWLv2 base model for use with Autodistill.
- Host: GitHub
- URL: https://github.com/autodistill/autodistill-owlv2
- Owner: autodistill
- License: apache-2.0
- Created: 2023-10-26T11:34:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-20T15:58:41.000Z (over 1 year ago)
- Last Synced: 2025-04-14T12:13:13.223Z (2 months ago)
- Topics: autodistill, computer-vision, object-detection, owlv2, zero-shot-object-detection
- Language: Python
- Homepage: https://docs.autodistill.com
- Size: 10.7 KB
- Stars: 4
- Watchers: 3
- Forks: 6
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Autodistill OWLv2 Module
This repository contains the code supporting the OWLv2 base model for use with [Autodistill](https://github.com/autodistill/autodistill).
OWLv2 is a zero-shot object detection model that follows from on the OWL-ViT architecture. OWLv2 has an open vocabulary, which means you can provide arbitrary text prompts for the model. You can use OWLv2 with autodistill for object detection.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [OWLv2 Autodistill documentation](https://autodistill.github.io/autodistill/base_models/owlv2/).
## Installation
To use OWLv2 with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-owlv2
```
## Quickstart
```python
from autodistill_owlv2 import OWLv2
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
import cv2
# define an ontology to map class names to our OWLv2 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 = OWLv2(
ontology=CaptionOntology(
{
"person": "person",
"dog": "dog"
}
)
)
# run inference on a single image
results = base_model.predict("dog.jpeg")
plot(
image=cv2.imread("dog.jpeg"),
classes=base_model.ontology.classes(),
detections=results
)
# label a folder of images
base_model.label("./context_images", extension=".jpeg")
```
## License
This model is licensed under an [Apache 2.0](LICENSE) ([see original model implementation license](https://huggingface.co/docs/transformers/main/en/model_doc/owlv2), and the corresponding [HuggingFace Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/owlv2)).
## 🏆 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!