TinyFormer

TinyFormer is a trainable transformer detector with a DINOv3 backbone.

Tasks: detection
Install: pip install libreyolo
Support tier: Core, since v1.6.0. Core trainable detectors: features follow the flagships in the same release wave.
Upstream: TinyFormer by TinyFormer authors, Apache-2.0; DINOv3 terms. Paper, source
Licenses: Code MIT, weights Apache-2.0; DINOv3 terms. Commercial use

Install

bash

pip install "libreyolo"

Predict

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreTinyFormers.pt", device="cpu")result = model(SAMPLE_IMAGE)print(result)

Choose a published checkpoint below. The naming distinguishes the VisDrone variants and Objects365-to-COCO variants. Prediction requires a square imgsz; rectangular inputs raise an error.

Train

Training accepts a detection dataset YAML. Omitted epochs, batch, imgsz, lr0 and amp use the family recipe.

Dataset setup describes the required training data.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreTinyFormers.pt", device="cpu")# coco8.yaml is a built-in 8-image detection dataset that downloads on first use.# Replace it with the path to your own detection dataset YAML.model.train(data="coco8.yaml", epochs=1, device="cpu", workers=0)

Validate

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreTinyFormers.pt", device="cpu")# coco8.yaml downloads on first use; pass your own detection dataset YAML instead.metrics = model.val(data="coco8.yaml", workers=0)print(metrics)

Use the dataset format for this task. Validation explains the dataset requirements and returned metrics.

Export

Task	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
Detection	Detection to ONNX: supported	Detection to TorchScript: supported	Detection to ExecuTorch: supported	Detection to TensorRT: supported	Detection to OpenVINO: supported	Detection to Paddle: not supported	Detection to MNN: not supported	Detection to RKNN: not supported	Detection to ncnn: not supported	Detection to TFLite: not supported	Detection to CoreML: not supported	Detection to Core AI: not supported

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreTinyFormers.pt", device="cpu")model.export(format="onnx")

Export setup lists format dependencies and loading exported artifacts.

Checkpoints

File	Weights license
Detection
LibreTinyFormers.pt	other
LibreTinyFormerm.pt	other
LibreTinyFormerl.pt	other
LibreTinyFormerx.pt	other
LibreTinyFormerxl.pt	other
LibreTinyFormers-visdrone.pt	other
LibreTinyFormerm-visdrone.pt	other
LibreTinyFormerl-visdrone.pt	other
LibreTinyFormerx-visdrone.pt	other
LibreTinyFormerx-obj2coco.pt	other
LibreTinyFormerxl-obj2coco.pt	other

Every file above exists in the LibreYOLO org today and downloads on first use.

Licensing

Check the license on the Hugging Face repository of the specific weights you download. Every checkpoint in the LibreYOLO org carries one, and they are not always the same across a family. That repository is the authoritative source; the summary below describes what applied when this page was last verified.

This is a description of the licenses involved, not legal advice. If the answer matters commercially, read the licenses yourself and take your own counsel.

Original work: TinyFormer, TinyFormer authors
Upstream license: Apache-2.0; DINOv3 terms
Upstream source: github.com/mmpmmpmmpjosh/TinyFormer
LibreYOLO code: MIT
Weights: Apache-2.0; DINOv3 terms, republished at huggingface.co/LibreYOLO
Interpretation: The detector uses Apache-2.0 source and a DINOv3 backbone with its own terms. Check the publisher declarations for the chosen checkpoint.

Citation

@article{hsieh2026tinyformer,

  title={TinyFormer: Preserving Tiny Objects in YOLO-DETR Hybrid Real-time Detectors},

  author={Hsieh, Jun-Wei and Kao, Meng-Yu and Kurniawan, Ghufron Wahyu and Peng, Kuan-Chuan},

  journal={arXiv preprint arXiv:2605.25046},

  year={2026}

}

Copied from the authors' citation block at raw.githubusercontent.com/mmpmmpmmpjosh/TinyFormer/main/README.md.