Practical overview
When it fits
Use it when you want a code-based starting point for Gemma 3 object-detection experiments, especially when exploring bounding boxes as generated text tokens or reproducing the license-plate example.
Who it is for
- Developers exploring vision-language model fine-tuning for object detection.
- Practitioners interested in representing bounding boxes as generated location tokens.
- Researchers or builders adapting the repository’s license-plate detection example.
Documented uses
- Fine-tune Gemma 3 for an object-detection task.
- Compare training with location tokens absent from the tokenizer against explicitly extending the tokenizer.
- Run the documented license-plate prediction example and generate output images.
- Reproduce or modify the example dataset using the included dataset script.
Requirements & limits
The repository is public and its code and documentation are available. No license information is supplied.
- The project supports two location-token strategies: implicit learning without tokenizer extension, or explicit extension using
--include_loc_tokens. - The documented dataset is a reformatted license-plate detection dataset; broader object classes are not presented as a completed example.
- The README uses Python 3.10, uv, and a requirements file, and puts key training settings in
config.py. - Low Rank Adaptation, quantized Low Rank Adaptation, and larger datasets are listed as areas for future investigation.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the fine-tuning environment
Create the documented Python environment and install the repository dependencies.
Before you start
- Git
- uv
- Python 3.10
Steps
Clone the repository: the command below.
git clone https://github.com/ariG23498/gemma3-object-detection.gitCreate a virtual environment: the command below.
uv venv .venv --python 3.10Activate it with
source .venv/bin/activate.Install dependencies with the command below.
uv pip install -r requirements.txt
Expected outcome
A Python 3.10 virtual environment with the listed repository dependencies installed.
Guide limitations
- These setup steps do not configure training parameters or run training.
Train and run the license-plate example
Configure the documented training workflow, train with location tokens, and generate example prediction images.
Before you start
- The repository and its dependencies set up in a Python 3.10 environment.
- Training settings configured in
config.py.
Steps
Set the training parameters, including model path and dataset, in
config.py.Run the command below to train with the extended location-token vocabulary.
python train.py --include_loc_tokensRun the command below to perform license-plate detection and generate images in the
outputs/folder.python predict.py
Expected outcome
The documented workflow produces license-plate prediction images in `outputs/`.
Guide limitations
- The README does not specify hardware requirements or training duration.
- This recipe describes the repository's license-plate example, not validated performance on other object classes.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 18 October 2026
- Original website title
- ariG23498/gemma3-object-detection · Official homepage title · retained in its source language