About
First stable[^1] release for **Glasses Detector**. Three tasks are supported for processing images with glasses: **classification**, **detection**, and **segmentation**. This includes glasses types and parts, as well as their shadows and eye-area. For all the available features and examples, please check [documentation](https://mantasu.github.io/glasses-detector/docs/features.html) and [notebook](https://colab.research.google.com/github/mantasu/glasses-detector/blob/main/notebooks/demo.ipynb).
> [!NOTE]
> Pre-trained weights for size `large` are not available as it seems most `medium` models perform better (less overfit due to tasks being binary). More experiments need to be conducted before `large` weights can be released in `v1.1.0`.
What's New
* **Features**
* Glasses and eye-area detection
* More categories for each task
* Better model performance
* Simpler CLI
* **Architecture**
* New package structure
* New model architectures
* Simpler customization process (better inheritance structure)
* **Data**
* More datasets
* Simpler data processing scripts
* Kaggle notebooks for sub-tasks
* **Documentation**
* Full documentation of package-specific code
* Features & examples
* Fancier documentation page style
Other
As before, although the code for training (e.g., datasets, metrics) is part of _PIP package_, the actual _data processing_, _training_, and _model analysis_ scripts are only available when fully cloning the repository. Please check [Data](https://github.com/mantasu/glasses-detector?tab=readme-ov-file#data) and [Running](https://github.com/mantasu/glasses-detector?tab=readme-ov-file#running) sections for details on how to train/test your own models.
[^1]: _Kind of stable_ since `large` weights are missing (technically not but they perform worse than `medium`)