LibreYOLO is a MIT licensed open source computer vision library
645
stars
2,547
commits
Python
primary language
Sep 10, 2026
updated
README
LibreYOLO
⭐ Support LibreYOLO. The best way to help is to star the repo. Feel free to open an issue if you encounter problems or have suggestions, and code contributions are very welcome (see CONTRIBUTING.md).
An MIT-licensed computer vision library. Detection, segmentation, pose, depth, OCR and a dozen more tasks behind one small API, with training and export included rather than sold separately. Reads common YOLO-format datasets, so existing workflows port over with minimal changes.
Install
pip install libreyolo
from libreyolo import LibreYOLO, SAMPLE_IMAGE
model = LibreYOLO("LibreYOLO9t.pt")
result = model(SAMPLE_IMAGE, save=True)
Optional extras
The base install covers YOLOv9 and the other core detectors, training, and
inference. Add an extra when you need a heavier family or an export backend.
Comma-separate to combine, for example pip install "libreyolo[rfdetr,onnx]".
| Group | Extras |
|---|---|
| Export | onnx, tensorrt, openvino, coreml, coreai, tflite (alias litert), ncnn, mnn, paddle, executorch |
| Serving | triton |
| Models | rfdetr, vlm, sam, openvocab, clip, siglip2, eomt, midas, modus, sensenova, gaze |
| Training | lora, plots, tensorboard, mlflow, wandb, comet, clearml, neptune, dvclive |
| Speed | fast-eval, hub-kernels |
| Sources | stream |
| Everything | pip install "libreyolo[all]" |
executorch, coreai and neptune are deliberately left out of all: they
pin torch or protobuf in ways that would drag the rest of the environment with
them. Full list and per-backend notes in the
install guide.
Install from source
git clone https://github.com/LibreYOLO/libreyolo.git
cd libreyolo
pip install -e .
A plain clone checks out release, the stable branch matching the published
package. For unreleased work, git checkout dev.
One API, seventeen tasks
The same three lines run every task. Only the checkpoint changes.
from libreyolo import LibreYOLO
LibreYOLO("LibreYOLO9t.pt")("street.jpg", save=True) # detection
LibreYOLO("LibreDeepLabv3mv3-sem.pt")("street.jpg", save=True) # semantic segmentation
LibreYOLO("LibreHRNetw32-pose.pt")("street.jpg", save=True) # pose
LibreYOLO("LibreMiDaSs-depth.pt")("street.jpg", save=True) # depth
LibreYOLO("LibreFeyNobgl-matte.pt")("portrait.jpg", save=True) # background removal
LibreYOLO("LibreRTDETRv2n-obb.pt")("aerial.jpg", save=True) # oriented boxes
Sources are not just files. Point it at a webcam, an RTSP stream, a video, a directory, a YouTube URL or your screen:
libreyolo predict --model yolo9-t --source 0 --show # webcam
libreyolo predict --model yolo9-t --source rtsp://camera/1 # network camera
libreyolo predict --model yolo9-t --source screen # screen capture
What ships
| Task | Models |
|---|---|
| Detection | YOLOv9, RF-DETR, YOLOX, YOLO-NAS, D-FINE, DEIM, RT-DETR v1/v2/v4, RTMDet, PicoDet, YOLOv7, EfficientDet, and the classics: DETR, Deformable DETR, DINO-DETR, LW-DETR, Faster R-CNN, RetinaNet, SSD, FCOS, CenterNet |
| Tiny objects | Dome-DETR (aerial, drone, remote sensing) |
| Instance segmentation | RF-DETR, RTMDet, D-FINE, Mask R-CNN |
| Promptable segmentation | SAM, SAM 2, SAM 3, MobileSAM, EdgeTAM, PicoSAM3 |
| Semantic segmentation | SegFormer, PIDNet, DeepLabv3, FCN, LingBot-Vision, DINOv2, EoMT |
| Panoptic segmentation | EoMT |
| Pose | RF-DETR, YOLO-NAS, HRNet, EC |
| Oriented boxes | RF-DETR, RT-DETRv2 |
| Classification | MobileNetV4, ConvNeXt, EfficientNetV2, ResNet, ViT, Swin, DeiT, VGG, AlexNet, CLIP, SigLIP2, DINOv2 |
| Depth | Depth Anything 3, Depth Anything V2, ZipDepth, MiDaS |
| Surface normals | MoGe-2 |
| Edges | DexiNed, TEED |
| Embeddings | LibreFaceEmbedder, CLIP, SigLIP2, DINOv2 |
| Body mesh | SAM 3D Body |
| Restoration | NAFNet, Real-ESRGAN, SwinIR |
| Background removal | BiRefNet, FeyNobg |
| OCR | PP-OCR |
| Point detection | FOMO, LocateAnything |
| Gaze | L2CS |
| Open vocabulary and VLMs | Grounding DINO, OWLv2, OmDet-Turbo, OV-DEIM, Florence-2, Kosmos-2, Qwen3-VL, InternVL3, LFM2-VL, SmolVLM2, MODUS |
Per-family sizes, checkpoints and parity evidence live in the model reference.
Train
from libreyolo import LibreYOLO
model = LibreYOLO("LibreYOLO9t.pt")
model.train(data="dataset.yaml", epochs=100, imgsz=640)
libreyolo train --model yolo9-t --data dataset.yaml --epochs 100
Multi-GPU, LoRA, layer freezing, distillation, from-scratch training, and TensorBoard, MLflow, Weights & Biases, Comet, ClearML, Neptune and DVCLive logging are all supported. See the training guide.
Export and deploy
Twelve formats: ONNX, TorchScript, TensorRT, OpenVINO, CoreML, Core AI, TFLite (LiteRT), NCNN, MNN, RKNN, Paddle and ExecuTorch. Plus NVIDIA Triton serving and DeepStream config generation.
libreyolo export --model yolo9-t --format onnx
Support varies by family and task, see the export matrix.
Documentation
- Docs covers install, tasks, models, training, prediction, export and the CLI
- Benchmarks for independent numbers
- CHANGELOG.md for what changed
License
- Code: MIT License.
- Weights: pre-trained weights may inherit licensing from their original source, and not all of them are permissive. Check the license on the specific Hugging Face repo before you use one commercially. Every LibreYOLO Hugging Face model states its license.
Contributors
Similar repos
Languages
Python
99.9%
LibreYOLO is a MIT licensed open source computer vision library
645
stars
2,547
commits
Python
primary language
Sep 10, 2026
updated
README
LibreYOLO
⭐ Support LibreYOLO. The best way to help is to star the repo. Feel free to open an issue if you encounter problems or have suggestions, and code contributions are very welcome (see CONTRIBUTING.md).
An MIT-licensed computer vision library. Detection, segmentation, pose, depth, OCR and a dozen more tasks behind one small API, with training and export included rather than sold separately. Reads common YOLO-format datasets, so existing workflows port over with minimal changes.
Install
pip install libreyolo
from libreyolo import LibreYOLO, SAMPLE_IMAGE
model = LibreYOLO("LibreYOLO9t.pt")
result = model(SAMPLE_IMAGE, save=True)
Optional extras
The base install covers YOLOv9 and the other core detectors, training, and
inference. Add an extra when you need a heavier family or an export backend.
Comma-separate to combine, for example pip install "libreyolo[rfdetr,onnx]".
| Group | Extras |
|---|---|
| Export | onnx, tensorrt, openvino, coreml, coreai, tflite (alias litert), ncnn, mnn, paddle, executorch |
| Serving | triton |
| Models | rfdetr, vlm, sam, openvocab, clip, siglip2, eomt, midas, modus, sensenova, gaze |
| Training | lora, plots, tensorboard, mlflow, wandb, comet, clearml, neptune, dvclive |
| Speed | fast-eval, hub-kernels |
| Sources | stream |
| Everything | pip install "libreyolo[all]" |
executorch, coreai and neptune are deliberately left out of all: they
pin torch or protobuf in ways that would drag the rest of the environment with
them. Full list and per-backend notes in the
install guide.
Install from source
git clone https://github.com/LibreYOLO/libreyolo.git
cd libreyolo
pip install -e .
A plain clone checks out release, the stable branch matching the published
package. For unreleased work, git checkout dev.
One API, seventeen tasks
The same three lines run every task. Only the checkpoint changes.
from libreyolo import LibreYOLO
LibreYOLO("LibreYOLO9t.pt")("street.jpg", save=True) # detection
LibreYOLO("LibreDeepLabv3mv3-sem.pt")("street.jpg", save=True) # semantic segmentation
LibreYOLO("LibreHRNetw32-pose.pt")("street.jpg", save=True) # pose
LibreYOLO("LibreMiDaSs-depth.pt")("street.jpg", save=True) # depth
LibreYOLO("LibreFeyNobgl-matte.pt")("portrait.jpg", save=True) # background removal
LibreYOLO("LibreRTDETRv2n-obb.pt")("aerial.jpg", save=True) # oriented boxes
Sources are not just files. Point it at a webcam, an RTSP stream, a video, a directory, a YouTube URL or your screen:
libreyolo predict --model yolo9-t --source 0 --show # webcam
libreyolo predict --model yolo9-t --source rtsp://camera/1 # network camera
libreyolo predict --model yolo9-t --source screen # screen capture
What ships
| Task | Models |
|---|---|
| Detection | YOLOv9, RF-DETR, YOLOX, YOLO-NAS, D-FINE, DEIM, RT-DETR v1/v2/v4, RTMDet, PicoDet, YOLOv7, EfficientDet, and the classics: DETR, Deformable DETR, DINO-DETR, LW-DETR, Faster R-CNN, RetinaNet, SSD, FCOS, CenterNet |
| Tiny objects | Dome-DETR (aerial, drone, remote sensing) |
| Instance segmentation | RF-DETR, RTMDet, D-FINE, Mask R-CNN |
| Promptable segmentation | SAM, SAM 2, SAM 3, MobileSAM, EdgeTAM, PicoSAM3 |
| Semantic segmentation | SegFormer, PIDNet, DeepLabv3, FCN, LingBot-Vision, DINOv2, EoMT |
| Panoptic segmentation | EoMT |
| Pose | RF-DETR, YOLO-NAS, HRNet, EC |
| Oriented boxes | RF-DETR, RT-DETRv2 |
| Classification | MobileNetV4, ConvNeXt, EfficientNetV2, ResNet, ViT, Swin, DeiT, VGG, AlexNet, CLIP, SigLIP2, DINOv2 |
| Depth | Depth Anything 3, Depth Anything V2, ZipDepth, MiDaS |
| Surface normals | MoGe-2 |
| Edges | DexiNed, TEED |
| Embeddings | LibreFaceEmbedder, CLIP, SigLIP2, DINOv2 |
| Body mesh | SAM 3D Body |
| Restoration | NAFNet, Real-ESRGAN, SwinIR |
| Background removal | BiRefNet, FeyNobg |
| OCR | PP-OCR |
| Point detection | FOMO, LocateAnything |
| Gaze | L2CS |
| Open vocabulary and VLMs | Grounding DINO, OWLv2, OmDet-Turbo, OV-DEIM, Florence-2, Kosmos-2, Qwen3-VL, InternVL3, LFM2-VL, SmolVLM2, MODUS |
Per-family sizes, checkpoints and parity evidence live in the model reference.
Train
from libreyolo import LibreYOLO
model = LibreYOLO("LibreYOLO9t.pt")
model.train(data="dataset.yaml", epochs=100, imgsz=640)
libreyolo train --model yolo9-t --data dataset.yaml --epochs 100
Multi-GPU, LoRA, layer freezing, distillation, from-scratch training, and TensorBoard, MLflow, Weights & Biases, Comet, ClearML, Neptune and DVCLive logging are all supported. See the training guide.
Export and deploy
Twelve formats: ONNX, TorchScript, TensorRT, OpenVINO, CoreML, Core AI, TFLite (LiteRT), NCNN, MNN, RKNN, Paddle and ExecuTorch. Plus NVIDIA Triton serving and DeepStream config generation.
libreyolo export --model yolo9-t --format onnx
Support varies by family and task, see the export matrix.
Documentation
- Docs covers install, tasks, models, training, prediction, export and the CLI
- Benchmarks for independent numbers
- CHANGELOG.md for what changed
License
- Code: MIT License.
- Weights: pre-trained weights may inherit licensing from their original source, and not all of them are permissive. Check the license on the specific Hugging Face repo before you use one commercially. Every LibreYOLO Hugging Face model states its license.
Similar repos
Contributors
Languages
Python
99.9%