VLM detection fine-tuning

Qwen3-VL fine-tuning trains language-model adapters against detection targets.

Install

bash

pip install "libreyolo[vlm-train]"

Train

Python

from libreyolo import LibreVLM model = LibreVLM("qwen3-vl-2b", device="cpu")model.train(data="coco8", epochs=1, batch=1, workers=0)

LoRA is the default. Full language-model tuning is optional; the vision tower stays frozen. The default learning rate is 1e-4 for LoRA or 2e-5 for full tuning. Defaults are 10 epochs, batch 1, accumulation 8, workers 0, horizontal-flip probability 0.5 and gradient checkpointing enabled.

Use a standard detection dataset YAML with .txt label files; its class names become the prompt vocabulary. COCO-JSON annotations are not supported. This workflow has its own training arguments and checkpoint format; detector LoRA uses a different trainer.

Reload

Training writes checkpoint directories under an incremented runs/vlm/train. Load a returned checkpoint directory with LibreVLM(path). Validation loss selects the best checkpoint, or training loss when the dataset has no val split. Optimizer-state resume and detection-mAP validation are not supported.