https://github.com/autodistill/autodistill-qwen-vl
Qwen-VL base model for use with Autodistill.
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Keywords
Repository
Qwen-VL base model for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://docs.autodistill.com
- Size: 6.84 KB
Statistics
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
Autodistill Qwen-VL Module
This repository contains the code supporting the Qwen-VL base model for use with Autodistill.
Qwen-VL, introduced in the paper Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond, is a multimodal vision model. Qwen-VL has visual grounding capabilities, which allows you to use the model for zero-shot object detection.
You can use Autodistill Qwen-VL to auto-label images for use in training a smaller, fine-tuned vision model.
Read the full Autodistill documentation.
Read the Qwen-VL Autodistill documentation.
Installation
To use Qwen-VL with Autodistill, you need to install the following dependency:
bash
pip3 install autodistill-qwen-vl
Quickstart
```python from autodistillqwenvl import QwenVL from autodistill.utils import plot from autodistill.detection import CaptionOntology
define an ontology to map class names to our QwenVL 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
basemodel = QwenVL( ontology=CaptionOntology( { "person": "person", "a forklift": "forklift" } ) ) results = basemodel.predict("logistics.jpeg")
plot( image=cv2.imread("logistics.jpeg"), classes=base_model.ontology.classes(), detections=results )
label all images in a folder called context_images
basemodel.label("./contextimages", extension=".jpeg") ```
License
[add license information here]
🏆 Contributing
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!
Owner
- Name: Autodistill
- Login: autodistill
- Kind: organization
- Email: autodistill@roboflow.com
- Website: https://autodistill.com
- Repositories: 1
- Profile: https://github.com/autodistill
Use bigger slower models to train smaller faster ones
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| James Gallagher | j****g@j****g | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 1
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 1.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 1.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- sulaiman1988 (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- autodistill *
- numpy *
- supervision *
- torch *
- transformers *