https://github.com/autodistill/autodistill-vlpart
VLPart model for use with Autodistill.
https://github.com/autodistill/autodistill-vlpart
autodistill computer-vision object-detection vlpart
Last synced: about 2 months ago
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VLPart model for use with Autodistill.
- Host: GitHub
- URL: https://github.com/autodistill/autodistill-vlpart
- Owner: autodistill
- License: mit
- Created: 2023-11-02T19:29:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-06T11:04:15.000Z (over 1 year ago)
- Last Synced: 2025-04-14T12:13:03.906Z (about 2 months ago)
- Topics: autodistill, computer-vision, object-detection, vlpart
- Language: Python
- Homepage: https://docs.autodistill.com
- Size: 24.4 KB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Autodistill VLPart Module
This repository contains the code supporting the VLPart base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[VLPart](https://github.com/facebookresearch/VLPart), developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary. `autodistill-vlpart` enables you to use VLPart to auto-label images for use in training a fine-tuned model. `autodistill-vlpart` supports the LVIS vocabulary.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [VLPart Autodistill documentation](https://autodistill.github.io/autodistill/base_models/vlpart/).
## Installation
To use VLPart with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-vlpart
```
## Quickstart
```python
from autodistill_vlpart import VLPart
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
# define an ontology to map class names to our VLPart 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 = VLPart(
ontology=CaptionOntology(
{
"person": "person"
}
)
)
predictions = base_model.predict("./image.png")
print(predictions)
plot(
image=cv2.imread("./image.png"),
classes=base_model.class_names,
detections=predictions
)
# label the images in the context_images folder
base_model.label("./context_images", extension=".jpeg")
```
## License
This project is licensed under an [MIT 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!