V0.2 - Ze-robot

ze-robot --source /path/to/raw_images --dest /path/to/processed_dataset | Flag | Description | |------|-------------| | --source | Input directory (recursively scanned) | | --dest | Output directory (created if nonexistent) | | --caption-source | adjacent , metadata , or filename | | --train-ratio | Float between 0 and 1 (default 0.9) | | --remove-duplicates | Flag to enable hash-based dedup | | --image-ext | Comma-separated list (default: jpg,jpeg,png,webp,bmp) | | --recursive | On by default in v0.2; can be disabled | | --seed | Integer for reproducible random splitting |

In an era of increasingly complex MLOps platforms, there is profound value in a tool that does . Ze-robot v0.2 converts chaos into structure. It doesn’t pretend to be intelligent—it just renames, pairs, and splits. Sometimes, that’s exactly what you need. ze-robot v0.2

: github.com/ze-robot/ze-robot (example; actual URL may differ) License : MIT Python version requirement : 3.8+ Have you used ze-robot v0.2 in a project? The maintainers welcome pull requests addressing the limitations mentioned above, especially UTF-8 robustness and large-dataset memory usage. Sometimes, that’s exactly what you need

In the sprawling ecosystem of open-source machine learning, certain tools gain quiet ubiquity. They are rarely the subject of conference keynotes, yet they appear in countless README.md files, automation scripts, and Colab notebooks. Ze-robot v0.2 is precisely such a tool. In the sprawling ecosystem of open-source machine learning,

Example with all options:

pip install ze-robot Basic usage: