# LibreYOLO: MIT-Licensed Object Detection

> LibreYOLO is the MIT-licensed training and inference engine for state-of-the-art YOLO models. Built for commercial applications, free from AGPL restrictions.

libreyolo.com is the official site of the LibreYOLO open-source library: documentation, model zoo, dataset zoo, commercial licensing guide, and articles. Pages are also served under a locale path prefix (e.g. https://www.libreyolo.com/zh/models) for: /zh (中文), /es (ES), /it (IT), /pt (PT), /fr (FR), /ru (RU), /id (ID), /vi (VI), /de (DE), /pl (PL), /ja (日本語), /ko (한국어), /uk (УКР). The documentation is fully translated in every one of those languages; articles are English and Chinese for now.

## Get started
- Install: `pip install libreyolo`
- [Documentation](https://www.libreyolo.com/docs): install, quickstart, every model family, every task, training, validation and export
- Every documentation page is also served as raw markdown by appending `.md` to its URL, for example https://www.libreyolo.com/docs/models/rf-detr.md
- [The whole documentation as one file](https://www.libreyolo.com/llms-full.txt)

## Key pages
- [Home](https://www.libreyolo.com/): project overview and quickstart
- [Model Zoo](https://www.libreyolo.com/models): pre-trained weights with MIT licensing, browsable per family
- [Benchmarks](https://www.libreyolo.com/benchmarks): measured COCO accuracy, RF100-VL transfer across 100 real-world datasets, and latency on real hardware
- [Dataset Zoo](https://www.libreyolo.com/datasets): datasets for training and evaluating models, hosted on Hugging Face
- [Commercial Guide](https://www.libreyolo.com/commercial): how to use LibreYOLO in proprietary, closed-source commercial applications under the MIT license
- [Research & Science](https://www.libreyolo.com/science): native explainability, feature maps, and a codebase designed for discovery
- [Articles](https://www.libreyolo.com/articles): tutorials, comparisons, and news about MIT-licensed object detection
- [LibreVLM](https://www.libreyolo.com/docs/librevlm): vision-language model documentation
- [Experimental tasks](https://www.libreyolo.com/docs/experimental): research previews and experimental model documentation
- [Cursor Hackathon track](https://www.libreyolo.com/cursor-hackathon): setup tutorial and working examples for the LibreYOLO track

## Documentation versions
https://www.libreyolo.com/docs always describes the current release. These single-page docs for
earlier releases stay reachable for anyone pinned to them, but they are frozen,
no longer updated, and canonicalised to https://www.libreyolo.com/docs. Prefer /docs.
- [v1.4.0 (frozen)](https://www.libreyolo.com/docs/v1.4.0)
- [v1.3.1 (frozen)](https://www.libreyolo.com/docs/v1.3.1)
- [v1.3.0 (frozen)](https://www.libreyolo.com/docs/v1.3.0)
- [v1.2.0 (frozen)](https://www.libreyolo.com/docs/v1.2.0)
- [v1.1.0 (frozen)](https://www.libreyolo.com/docs/v1.1.0)

## Documentation
Generated from the docs tree, so this list cannot drift from what is published.
### Getting started
- [Changelog](https://www.libreyolo.com/docs/changelog): What landed in LibreYOLO 1.5.0 and what 1.4.0 contained: 28 new model families, four new tasks, five export formats, torch-free ONNX inference and faster COCO metrics.
- [Citation](https://www.libreyolo.com/docs/citation): How to cite LibreYOLO in a paper, and how to cite the authors of the model family you ran. Both belong in the same methods section.
- [Core concepts](https://www.libreyolo.com/docs/concepts): How tasks, model families, sizes and checkpoint filenames fit together in LibreYOLO, and what each support tier promises.
- [FAQ](https://www.libreyolo.com/docs/faq): Short answers to the questions that cut across every LibreYOLO model: hardware, licensing, weights, devices, training, export coverage and the CLI.
- [Install](https://www.libreyolo.com/docs/install): Install LibreYOLO from PyPI, pick the optional extras a model family or export target needs, and confirm PyTorch sees your GPU.
- [Licensing](https://www.libreyolo.com/docs/licensing): LibreYOLO's own code is MIT. Vendored upstream code and published checkpoints carry their own licenses, and several of those are non-commercial.
- [Lightweight install](https://www.libreyolo.com/docs/lightweight-install): Install LibreYOLO with --no-deps and run ONNX detection on numpy alone, no torch on disk. The technique, its limits, and the exact package list.
- [Import existing weights](https://www.libreyolo.com/docs/migrate): Point LibreYOLO at a checkpoint from an upstream project. Autoconversion rewraps it at load time, keeping its class count and names.
- [Quickstart](https://www.libreyolo.com/docs/quickstart): Run a detector on an image, fine-tune it on a small dataset and export it to TorchScript or ONNX, all on CPU, in about ten lines of Python.
- [Troubleshooting](https://www.libreyolo.com/docs/troubleshooting): The errors LibreYOLO raises most often, what each one means, and the fix. Includes two failures that produce wrong output instead of raising.
- [Upgrading to 1.5.0](https://www.libreyolo.com/docs/upgrade): The four code changes 1.5.0 requires, the three changes that move metrics, and the smaller behavior shifts worth knowing before you compare runs.
- [Versions](https://www.libreyolo.com/docs/versions): Which release these pages describe, where the documentation for 1.1.0 through 1.4.0 lives, and how to check the version you have installed.
- [Checkpoints and weights](https://www.libreyolo.com/docs/weights): How LibreYOLO finds, downloads and verifies model weights, where they are hosted, how to run with no network, and what makes a checkpoint load safely.

### Tasks
- [Background removal](https://www.libreyolo.com/docs/tasks/background-removal): Cut a subject out of its background in LibreYOLO. Predict a soft alpha matte, write a transparent PNG, and validate with MAE and S-measure.
- [Body mesh](https://www.libreyolo.com/docs/tasks/body-mesh): Recover a parametric 3D body mesh per person in LibreYOLO. Predict from person boxes or a detector, and read vertices, joints and camera translation.
- [Depth estimation](https://www.libreyolo.com/docs/tasks/depth-estimation): Predict a dense relative depth map from one image in LibreYOLO. Compare the depth families, read the depth metrics, and export a depth model.
- [Edge detection](https://www.libreyolo.com/docs/tasks/edge-detection): Predict a dense edge-probability map from one image in LibreYOLO. Convert a checkpoint, threshold the map, validate with ODS and OIS, and export.
- [Embeddings](https://www.libreyolo.com/docs/tasks/embeddings): The embed task returns L2-normalized float32 vectors for a whole image, for each detected region, or for text. Enroll a gallery, match by cosine similarity, and search from Python or the CLI.
- [Face recognition](https://www.libreyolo.com/docs/tasks/face-recognition): Detect, embed and identify faces in LibreYOLO. Enroll a gallery, compare two images and match by cosine similarity, from Python or the CLI.
- [Gaze estimation](https://www.libreyolo.com/docs/tasks/gaze-estimation): Estimate per-face gaze pitch and yaw in LibreYOLO. Predict from Python or the CLI, read angles in radians, and export the gaze head to ONNX.
- [Image classification](https://www.libreyolo.com/docs/tasks/image-classification): Label a whole image in LibreYOLO: the families that serve the task, the ImageFolder dataset layout, and the predict, train, validate and export calls.
- [Image restoration](https://www.libreyolo.com/docs/tasks/image-restoration): Denoise, deblur and upscale images in LibreYOLO. Predict a restored RGB image, train NAFNet on paired data, and read the PSNR and SSIM keys.
- [Instance segmentation](https://www.libreyolo.com/docs/tasks/instance-segmentation): Segment individual objects in LibreYOLO: the families that serve the task, the polygon label format, and the predict, train, validate and export calls.
- [Object detection](https://www.libreyolo.com/docs/tasks/object-detection): Detect objects as axis-aligned boxes in LibreYOLO: the families that serve the task, the label format, and the predict, train, validate and export calls.
- [Object tracking](https://www.libreyolo.com/docs/tasks/object-tracking): Track objects across video frames in LibreYOLO with ByteTrack, BoT-SORT, OC-SORT or Deep OC-SORT, over any detection, segmentation or pose model.
- [OCR](https://www.libreyolo.com/docs/tasks/ocr): Find and read text in images with LibreYOLO. Predict quads and transcripts, label a JSONL dataset, and validate with hmean, end-to-end F1 and 1-NED.
- [Open-vocabulary detection](https://www.libreyolo.com/docs/tasks/open-vocabulary-detection): Detect objects from a text vocabulary in LibreYOLO. Load Grounding DINO, OWLv2, OMDet-Turbo or OV-DEIM through LibreOpenVocab and set classes at runtime.
- [Oriented detection](https://www.libreyolo.com/docs/tasks/oriented-detection): Detect rotated objects in LibreYOLO: the families that serve oriented boxes, the four-corner label row, and the predict, train, validate and export calls.
- [Panoptic segmentation](https://www.libreyolo.com/docs/tasks/panoptic-segmentation): Assign every pixel one segment in LibreYOLO: the families that serve the task, the COCO-panoptic dataset format, and the predict and validate calls.
- [Point detection](https://www.libreyolo.com/docs/tasks/point-detection): Locate objects as single points instead of boxes in LibreYOLO. Predict centroids, count objects, train FOMO, and read the point metrics.
- [Pose estimation](https://www.libreyolo.com/docs/tasks/pose-estimation): Predict keypoints per instance in LibreYOLO: the families that serve the task, the label format, and the predict, train, validate and export calls.
- [Promptable segmentation](https://www.libreyolo.com/docs/tasks/promptable-segmentation): Turn a point, box or text concept into an object mask in LibreYOLO. Load SAM, SAM 2, SAM 3, EdgeTAM, MobileSAM or PicoSAM3 through LibreSAM.
- [Semantic segmentation](https://www.libreyolo.com/docs/tasks/semantic-segmentation): Label every pixel with a class in LibreYOLO: the families that serve the task, the dense mask format, and the predict, train, validate and export calls.
- [Surface normals](https://www.libreyolo.com/docs/tasks/surface-normals): Predict a dense surface-normal field from one image in LibreYOLO. Read the camera frame convention, validate angular error, and export a model.

### Models
- [AlexNet](https://www.libreyolo.com/docs/models/alexnet): Predict, validate and export AlexNet with LibreYOLO. BSD-3-Clause torchvision weights; fine-tuning is not yet supported.
- [BiRefNet](https://www.libreyolo.com/docs/models/birefnet): Use BiRefNet in LibreYOLO for background removal and dichotomous image segmentation. Install, predict, validate and export the general checkpoint.
- [CenterNet](https://www.libreyolo.com/docs/models/centernet): Run CenterNet (Objects as Points) in LibreYOLO with the ResDCN-18 and DLA-34 backbones. Predict, validate and export to ONNX under MIT. No training path.
- [CLIP](https://www.libreyolo.com/docs/models/clip): Use CLIP in LibreYOLO for zero-shot image classification and image/text embedding. No training: set_classes() defines the label set at runtime.
- [ConvNeXt](https://www.libreyolo.com/docs/models/convnext): Use ConvNeXt in LibreYOLO for image classification. Install, predict, fine-tune with LoRA, validate and export LibreConvNeXt tiny/small/base.
- [D-FINE](https://www.libreyolo.com/docs/models/d-fine): Use D-FINE in LibreYOLO for object detection and instance segmentation. Install, predict, fine-tune, validate and export, with MIT-licensed code.
- [DeepLabv3](https://www.libreyolo.com/docs/models/deeplabv3): Use DeepLabv3 in LibreYOLO for semantic segmentation. Install, predict, validate and export torchvision's ResNet and MobileNetV3 checkpoints.
- [Deformable DETR](https://www.libreyolo.com/docs/models/deformable-detr): Run Deformable DETR in LibreYOLO for object detection. Install, predict, validate and export five sparse-attention sizes, all Apache-2.0 licensed.
- [DEIM](https://www.libreyolo.com/docs/models/deim): Use DEIM and DEIMv2 in LibreYOLO for object detection. Install, predict, train, validate and export, from a half-million-parameter size upward.
- [DeiT](https://www.libreyolo.com/docs/models/deit): Run DeiT image classifiers in LibreYOLO: a frozen, inference-only museum family in tiny, small and base sizes, under Apache-2.0.
- [Depth Anything 3](https://www.libreyolo.com/docs/models/depth-anything-3): Use Depth Anything 3 in LibreYOLO for monocular depth estimation. Install, predict, validate and export the DA3MONO-LARGE checkpoint, Apache-2.0.
- [Depth Anything V2](https://www.libreyolo.com/docs/models/depth-anything-v2): Use Depth Anything V2 in LibreYOLO for monocular depth estimation. Install, predict and validate; Small ships Apache-2.0, Base and Large are CC-BY-NC-4.0.
- [DETR](https://www.libreyolo.com/docs/models/detr): Run DETR, the original detection transformer, in LibreYOLO. Install, predict, validate and export four ResNet-based sizes, all Apache-2.0 licensed.
- [DexiNed](https://www.libreyolo.com/docs/models/dexined): Use DexiNed in LibreYOLO for dense edge-probability prediction. Convert a licensed checkpoint, then predict, validate and export it.
- [DINO-DETR](https://www.libreyolo.com/docs/models/dino-detr): Run DINO-DETR in LibreYOLO for object detection. Install, predict, validate and export three denoising-anchor sizes, all Apache-2.0 licensed.
- [DINOv2](https://www.libreyolo.com/docs/models/dinov2): Use DINOv2 in LibreYOLO for semantic segmentation, classification and whole-image embedding on the DINOv2-with-Registers backbone. Apache-2.0 throughout.
- [Dome-DETR](https://www.libreyolo.com/docs/models/dome-detr): Use Dome-DETR in LibreYOLO for tiny-object detection on aerial and drone imagery. Convert the upstream weights, predict, fine-tune and validate under MIT-licensed code.
- [EdgeCrafter](https://www.libreyolo.com/docs/models/edgecrafter): Use EdgeCrafter in LibreYOLO for detection, pose and instance segmentation. Install, predict, validate and export, with MIT-licensed code.
- [EdgeTAM](https://www.libreyolo.com/docs/models/edgetam): Use EdgeTAM in LibreYOLO for promptable point and box segmentation built for on-device speed. Install and predict the checkpoint under Apache-2.0.
- [EfficientDet](https://www.libreyolo.com/docs/models/efficientdet): Run EfficientDet D0-D4 in LibreYOLO: BiFPN detectors for prediction, validation and export to ONNX, TensorRT and OpenVINO under Apache-2.0.
- [EfficientNetV2](https://www.libreyolo.com/docs/models/efficientnetv2): Use EfficientNetV2 in LibreYOLO for image classification. Install, predict, fine-tune, validate and export LibreEfficientNetV2 b0 to b3.
- [EoMT](https://www.libreyolo.com/docs/models/eomt): Use EoMT in LibreYOLO for semantic, instance and panoptic segmentation on a plain DINOv2 vision transformer, no decoder needed. MIT-licensed.
- [Faster R-CNN](https://www.libreyolo.com/docs/models/faster-rcnn): Run Faster R-CNN in LibreYOLO for object detection across four backbones. Install, predict, validate and export the BSD-3-Clause torchvision port.
- [FCN](https://www.libreyolo.com/docs/models/fcn): Use FCN in LibreYOLO for semantic segmentation. Install, predict, validate and export torchvision's dilated-ResNet FCN checkpoints.
- [FCOS](https://www.libreyolo.com/docs/models/fcos): Run FCOS in LibreYOLO for anchor-free object detection. Install, predict, validate and export the BSD-3-Clause torchvision port, ResNet-50/FPN.
- [FeyNobg](https://www.libreyolo.com/docs/models/feynobg): Use FeyNobg in LibreYOLO for background removal and alpha matting, a deepened BiRefNet variant from Feyn Inc. Install, predict and validate.
- [Florence-2](https://www.libreyolo.com/docs/models/florence-2): Florence-2 in LibreYOLO: install, set an open vocabulary and predict boxes with Microsoft's MIT-licensed vision model.
- [FOMO](https://www.libreyolo.com/docs/models/fomo): Run FOMO (Faster Objects, More Objects) in LibreYOLO: a tiny point-localization detector for counting many small objects. Install, predict, train and export.
- [Grounding DINO](https://www.libreyolo.com/docs/models/grounding-dino): Use Grounding DINO in LibreYOLO to detect any text-described object. Install the openvocab extra and predict with a free-text vocabulary.
- [HRNet](https://www.libreyolo.com/docs/models/hrnet): Use HRNet in LibreYOLO for top-down COCO-17 pose estimation. Install, predict, validate and export the W32 and W48 checkpoints, MIT-licensed.
- [InternVL3](https://www.libreyolo.com/docs/models/internvl3): Use InternVL3 in LibreYOLO for open-vocabulary object detection. Predict with any text label; training, validation and export are not supported.
- [Kosmos-2](https://www.libreyolo.com/docs/models/kosmos-2): Kosmos-2 in LibreYOLO: install, set an open vocabulary and predict grounded boxes with Microsoft's MIT-licensed model.
- [L2CS-Net](https://www.libreyolo.com/docs/models/l2cs): Use L2CS-Net in LibreYOLO for two-stage gaze pitch/yaw estimation. Install, predict and export; the Gaze360 checkpoint is research-only.
- [LFM2-VL](https://www.libreyolo.com/docs/models/lfm2-vl): Use LFM2-VL in LibreYOLO for open-vocabulary object detection on-device. Predict with any text label; training, validation and export are not supported.
- [LibreFaceRec](https://www.libreyolo.com/docs/models/librefacerec): Use LibreFaceRec in LibreYOLO for face detection, embedding and verification. Install and predict; the embedding weights are Apache-2.0.
- [LibreMODUS](https://www.libreyolo.com/docs/models/libremodus): Use LibreMODUS in LibreYOLO for depth, normals, edges and detection, and to compose them with any2any(). Inference-only; weights load from EPFL-VILAB.
- [LingBot-Vision](https://www.libreyolo.com/docs/models/lingbot-vision): Use LingBot-Vision in LibreYOLO for semantic segmentation on an Apache-2.0 ViT backbone. Install, predict, train, validate and export, sizes s/b/l.
- [LocateAnything](https://www.libreyolo.com/docs/models/locate-anything): Use LocateAnything in LibreYOLO for open-vocabulary detection and pointing. Predict with any text label; training, validation and export are not supported.
- [LW-DETR](https://www.libreyolo.com/docs/models/lw-detr): Run LW-DETR in LibreYOLO for real-time object detection. Install, predict, validate and export five ViT-based sizes, all Apache-2.0 licensed.
- [Mask R-CNN](https://www.libreyolo.com/docs/models/mask-rcnn): Run Mask R-CNN in LibreYOLO for object detection and instance segmentation. Install, predict, validate and export the BSD-3-Clause torchvision port.
- [MiDaS](https://www.libreyolo.com/docs/models/midas): Use MiDaS in LibreYOLO for monocular depth estimation. Install, predict, validate and export two MIT-licensed variants, downloaded from isl-org.
- [MobileNetV4](https://www.libreyolo.com/docs/models/mobilenetv4): Use MobileNetV4 in LibreYOLO for image classification. Install, predict, fine-tune, validate and export LibreMobileNetV4 small/medium/large.
- [MobileSAM](https://www.libreyolo.com/docs/models/mobilesam): Use MobileSAM in LibreYOLO for promptable point and box segmentation with a TinyViT encoder. Install and predict the tiny checkpoint under Apache-2.0.
- [MoGe-2](https://www.libreyolo.com/docs/models/moge-2): Use MoGe-2 in LibreYOLO for dense surface-normal prediction. Install, predict, validate and export the official ViT-S, ViT-B and ViT-L checkpoints.
- [NAFNet](https://www.libreyolo.com/docs/models/nafnet): Use NAFNet in LibreYOLO for image denoising and restoration. Install, predict, train, validate and export the SIDD checkpoint, MIT-licensed.
- [OMDet-Turbo](https://www.libreyolo.com/docs/models/omdet-turbo): Use OMDet-Turbo in LibreYOLO for real-time open-vocabulary detection. Install the openvocab extra and predict with a free-text vocabulary.
- [OV-DEIM](https://www.libreyolo.com/docs/models/ov-deim): Use OV-DEIM in LibreYOLO for real-time, DETR-style open-vocabulary detection. Install the openvocab extra and predict with a free-text vocabulary.
- [OWLv2](https://www.libreyolo.com/docs/models/owlv2): Use OWLv2 in LibreYOLO to detect any text-described object. Install the openvocab extra and predict with a free-text vocabulary.
- [PicoDet](https://www.libreyolo.com/docs/models/picodet): Run PicoDet in LibreYOLO for mobile object detection. Install, predict, train, validate and export under Apache-2.0.
- [PicoSAM3](https://www.libreyolo.com/docs/models/picosam3): Use PicoSAM3 in LibreYOLO for box-prompted region segmentation on edge sensors. Install, predict and export the pico checkpoint under Apache-2.0.
- [PIDNet](https://www.libreyolo.com/docs/models/pidnet): Use PIDNet in LibreYOLO for real-time semantic segmentation. Install, predict, validate and export the s/m/l Cityscapes checkpoints under MIT.
- [PP-OCRv5](https://www.libreyolo.com/docs/models/pp-ocrv5): Use PP-OCRv5 in LibreYOLO for multilingual scene-text OCR. Install, predict and validate the t and l checkpoints, Apache-2.0 licensed.
- [Qwen3-VL](https://www.libreyolo.com/docs/models/qwen3-vl): Qwen3-VL in LibreYOLO: install, set an open vocabulary and predict or chat with Alibaba's Apache-2.0 vision-language model.
- [Real-ESRGAN](https://www.libreyolo.com/docs/models/real-esrgan): Use Real-ESRGAN in LibreYOLO for practical image super-resolution at 4x, 2x and a fast 4x tier. Install, predict, validate and export.
- [ResNet](https://www.libreyolo.com/docs/models/resnet): Use ResNet in LibreYOLO for image classification. Install, predict, fine-tune, validate and export LibreResNet18/34/50/101.
- [RetinaNet](https://www.libreyolo.com/docs/models/retinanet): Run RetinaNet in LibreYOLO for one-stage object detection with focal loss. Install, predict, validate and export the BSD-3-Clause torchvision port.
- [RF-DETR](https://www.libreyolo.com/docs/models/rf-detr): Use RF-DETR in LibreYOLO for detection, instance segmentation, pose and oriented boxes. Install, predict, train, validate and export, all MIT-licensed.
- [RT-DETR](https://www.libreyolo.com/docs/models/rt-detr): Use RT-DETR, RT-DETRv2 and RT-DETRv4 in LibreYOLO for object detection, plus oriented boxes on RT-DETRv2. Install, predict, train, validate and export, with Apache-2.0 weights.
- [RTMDet](https://www.libreyolo.com/docs/models/rtmdet): Run RTMDet in LibreYOLO for object detection and RTMDet-Ins instance segmentation. Install, predict, train, validate and export under Apache-2.0.
- [SAM](https://www.libreyolo.com/docs/models/sam): Use SAM in LibreYOLO for promptable point and box segmentation. Install and predict with the base, large and huge checkpoints under Apache-2.0.
- [SAM 2](https://www.libreyolo.com/docs/models/sam-2): Use SAM 2 in LibreYOLO for promptable point and box segmentation. Install and predict the tiny, small, base-plus and large checkpoints, Apache-2.0.
- [SAM 3](https://www.libreyolo.com/docs/models/sam-3): Use SAM 3 in LibreYOLO for point, box and text-concept segmentation. Install and predict the large checkpoint, gated under Meta's SAM License.
- [SAM 3D Body](https://www.libreyolo.com/docs/models/sam-3d-body): Use SAM 3D Body in LibreYOLO for full-body human mesh recovery. Install and predict; Meta's SAM License gates the checkpoints, CUDA required.
- [SegFormer](https://www.libreyolo.com/docs/models/segformer): Use SegFormer in LibreYOLO for ADE20K semantic segmentation across sizes b0-b5. Install, predict, train and export; pretrained weights are non-commercial.
- [SenseNova-Vision](https://www.libreyolo.com/docs/models/sensenova-vision): Use SenseNova-Vision in LibreYOLO for detection, segmentation, panoptic, pose, points, depth and OCR from one prompted generative checkpoint.
- [SigLIP2](https://www.libreyolo.com/docs/models/siglip2): Use SigLIP2 in LibreYOLO for zero-shot image classification and image/text embedding, with sigmoid multi-label scoring. No training needed.
- [SmolVLM2](https://www.libreyolo.com/docs/models/smolvlm2): SmolVLM2 in LibreYOLO: install, set an open vocabulary and predict or chat with Hugging Face's Apache-2.0 vision-language model.
- [SSD](https://www.libreyolo.com/docs/models/ssd): Run SSD300 in LibreYOLO: a single-shot VGG16 detector for prediction, validation and ONNX export under BSD-3-Clause. No training path.
- [Swin Transformer](https://www.libreyolo.com/docs/models/swin): Predict, validate and export Swin Transformer classifiers with LibreYOLO. MIT weights; fine-tuning is not yet supported.
- [SwinIR](https://www.libreyolo.com/docs/models/swinir): Use SwinIR in LibreYOLO for 4x image super-resolution. Install, predict, validate and export the lightweight, medium and large checkpoints.
- [TEED](https://www.libreyolo.com/docs/models/teed): Use TEED in LibreYOLO for dense edge-probability prediction. Convert a licensed checkpoint, then predict, validate and export it.
- [VGG](https://www.libreyolo.com/docs/models/vgg): Predict, validate and export VGG classifiers with LibreYOLO. BSD-3-Clause torchvision weights; fine-tuning is not yet supported.
- [ViT](https://www.libreyolo.com/docs/models/vit): Predict, validate and export ViT classifiers with LibreYOLO. Apache-2.0 AugReg weights; fine-tuning is not yet supported.
- [YOLO-NAS](https://www.libreyolo.com/docs/models/yolo-nas): Use YOLO-NAS in LibreYOLO for detection and pose. Deci.AI's weights are proprietary and non-commercial, and LibreYOLO publishes none of them.
- [YOLOv1](https://www.libreyolo.com/docs/models/yolov1): Run the original YOLOv1 detector in LibreYOLO: a frozen, inference-only museum family. Predict, validate and export, under a public-domain license.
- [YOLOv2](https://www.libreyolo.com/docs/models/yolov2): Run YOLOv2 (YOLO9000) in LibreYOLO: a frozen, inference-only museum family. Predict, validate and export, under a public-domain license.
- [YOLOv3](https://www.libreyolo.com/docs/models/yolov3): Run YOLOv3 in LibreYOLO: a frozen, inference-only museum family with tiny, base and SPP sizes. Predict, validate and export, under a public-domain license.
- [YOLOv4](https://www.libreyolo.com/docs/models/yolov4): Run YOLOv4 in LibreYOLO: a frozen, inference-only museum family with a CSPDarknet-53 backbone. Predict, validate and export, under a public-domain license.
- [YOLOv7](https://www.libreyolo.com/docs/models/yolov7): Run YOLOv7 in LibreYOLO for object detection: install, predict, train, validate and export, MIT-licensed code and weights.
- [YOLOv9](https://www.libreyolo.com/docs/models/yolov9): Run YOLOv9 in LibreYOLO, including the NMS-free end-to-end head and the stride-4 small-object head. Install, predict, train, validate and export.
- [YOLOX](https://www.libreyolo.com/docs/models/yolox): Use YOLOX in LibreYOLO for object detection: install, predict, train, validate and export under Apache-2.0.
- [ZipDepth](https://www.libreyolo.com/docs/models/zipdepth): Use ZipDepth in LibreYOLO for lightweight monocular depth estimation. Install, predict, validate and export two MIT-licensed checkpoints.

### Training
- [Augmentations](https://www.libreyolo.com/docs/train/augmentations): The augmentation knobs on TrainConfig, the four pipeline shapes behind them, and the per-family table saying which knobs are used, gated or ignored.
- [Training on a rented GPU](https://www.libreyolo.com/docs/train/cloud-gpus): Run a LibreYOLO training job on a rented or serverless GPU: stage the data, install, launch, watch it live, retrieve the weights and stop paying.
- [Datasets](https://www.libreyolo.com/docs/train/datasets): The dataset YAML LibreYOLO reads, the folder layout it expects, how autodownload works, and the doctor command that checks a dataset before training.
- [Knowledge distillation](https://www.libreyolo.com/docs/train/distillation): Train a small detector against a larger teacher or a frozen DINOv2 backbone: the MGD, CWD and feature-MSE losses, tap points, and family support.
- [Hyperparameters](https://www.libreyolo.com/docs/train/hyperparameters): The train() arguments that matter: epochs, batch, lr0, optimizer, EMA, autobatch, gradient accumulation and resume, plus why defaults differ per family.
- [Layer freezing](https://www.libreyolo.com/docs/train/layer-freezing): Freeze part of a model for transfer learning: an integer count of family freeze groups, an explicit index list, or module and parameter name selectors.
- [Experiment loggers](https://www.libreyolo.com/docs/train/loggers): Send training metrics to TensorBoard, MLflow, Weights & Biases, Comet, ClearML, Neptune or DVCLive, and write your own callback on the four training hooks.
- [LoRA fine-tuning](https://www.libreyolo.com/docs/train/lora): Fine-tune a transformer detector on low VRAM with lora=True. Which nine families support it, the per-family adapter recipe, and how the checkpoints behave.
- [Multi-GPU training](https://www.libreyolo.com/docs/train/multi-gpu): Train on several GPUs with device="0,1". How the library spawns DDP workers, why batch is the global batch, when to set sync_bn, and the torchrun path.
- [Training performance](https://www.libreyolo.com/docs/train/performance): Make a training run faster: capture the step into CUDA graphs, pick an AMP dtype, and use the built-in profiler to find where the time actually goes.
- [Validation and metrics](https://www.libreyolo.com/docs/train/validation): Run val() on any model, read the metric keys each task returns, choose an evaluation backend, and turn on a validation loss alongside the accuracy metric.

### Prediction
- [Ensembling detectors](https://www.libreyolo.com/docs/predict/ensembling): Run several detectors on one image and fuse their boxes with weighted boxes fusion or NMS, including models with different class lists.
- [Inference performance](https://www.libreyolo.com/docs/predict/performance): CUDA graphs, half precision, batching, tiled inference and test-time augmentation at predict time, with the real defaults and which families support each.
- [Working with results](https://www.libreyolo.com/docs/predict/results): One Results object per image, with a slot per payload type: boxes, masks, keypoints, probs, depth, panoptic, OCR and more. Plotting, saving and JSON.
- [Prediction sources](https://www.libreyolo.com/docs/predict/sources): Every source predict accepts: images, folders, URLs, video files, webcams, RTSP, YouTube, screen capture, image lists and .streams files.
- [Thresholds and filtering](https://www.libreyolo.com/docs/predict/thresholds): What conf, iou, max_det and classes actually do at predict time, which families ignore iou because they run no NMS, and why agnostic_nms is a no-op.

### Export and deploy
- [Core AI](https://www.libreyolo.com/docs/export/coreai): Export a LibreYOLO model to an Apple Core AI .aimodel asset: macOS only, fixed canvas, FP32, and the named-output ordering contract consumers must respect.
- [Core ML](https://www.libreyolo.com/docs/export/coreml): Export a LibreYOLO detector to a Core ML .mlpackage: the ImageType input contract, FP16, compute units, embedded NMS, and the four supported families.
- [NVIDIA DeepStream](https://www.libreyolo.com/docs/export/deepstream): Export a LibreYOLO model for NVIDIA DeepStream: an ONNX graph plus a generated nvinfer config. Exact commands for the parser build and the pipeline.
- [ExecuTorch](https://www.libreyolo.com/docs/export/executorch): Export a LibreYOLO model to an ExecuTorch .pte program with XNNPACK delegation: fixed shape, batch 1, FP32, and the metadata sidecar it needs.
- [Hailo](https://www.libreyolo.com/docs/export/hailo): Deploy a LibreYOLO model to a Hailo-8 or Hailo-8L: the static ONNX export, the Dataflow Compiler stage you run yourself, and which architectures compile.
- [NVIDIA Jetson](https://www.libreyolo.com/docs/export/jetson): Install LibreYOLO on an NVIDIA Jetson: the four CUDA libraries JetPack leaves out, the --no-deps step PyTorch needs, and measured Orin Nano numbers.
- [MNN](https://www.libreyolo.com/docs/export/mnn): Export a LibreYOLO detector to MNN through ONNX and mnnconvert: a fixed NCHW shape, FP32 on CPU, and a metadata sidecar the runtime contract requires.
- [ncnn](https://www.libreyolo.com/docs/export/ncnn): Export a LibreYOLO model to ncnn through PNNX: the param and bin pair, the fixed export canvas, the YOLOX Focus rewrite, and which families convert.
- [ONNX](https://www.libreyolo.com/docs/export/onnx): Export a LibreYOLO model to ONNX: the opset LibreYOLO picks per family, dynamic axes, embedded NMS, INT8, and how the graph loads back.
- [OpenVINO](https://www.libreyolo.com/docs/export/openvino): Convert a LibreYOLO model to OpenVINO IR: the model.xml and model.bin pair, FP16 weight compression, NNCF INT8, and CPU, GPU or NPU inference.
- [Paddle](https://www.libreyolo.com/docs/export/paddle): Convert a LibreYOLO detector to a PaddlePaddle inference model through X2Paddle: the pinned toolchain, static batch-1 FP32 graphs, and CPU inference.
- [Quantization](https://www.libreyolo.com/docs/export/quantization): LibreYOLO's PyTorch quantization API: nine recipes, calibration kept apart from training data, QAT and QAD, and two deployment artifacts.
- [RKNN](https://www.libreyolo.com/docs/export/rknn): Compile a LibreYOLO detector to a Rockchip .rknn artifact: the vendor SDK you install yourself, the four validated RK3588 variants, and simulator parity.
- [TensorRT](https://www.libreyolo.com/docs/export/tensorrt): Build a TensorRT engine from a LibreYOLO model: the ONNX intermediate, FP16 and INT8 builds, dynamic batch profiles, and engine portability limits.
- [TFLite](https://www.libreyolo.com/docs/export/tflite): Export a LibreYOLO model to a .tflite FlatBuffer through onnx2tf: static shapes, FP32 only, NHWC inputs, and the families that convert cleanly.
- [TorchScript](https://www.libreyolo.com/docs/export/torchscript): Export a LibreYOLO model to TorchScript: a traced .torchscript archive with LibreYOLO metadata inside, loadable from Python or libtorch.
- [Triton Inference Server](https://www.libreyolo.com/docs/export/triton): Serve a LibreYOLO ONNX export through NVIDIA Triton: the model repository layout, the generated config.pbtxt, and predicting against an HTTP model URL.

### Command line
- [libreyolo doctor](https://www.libreyolo.com/docs/cli/doctor): Check a detection dataset before training: arguments with defaults, the check families you can skip or select, and the exit codes CI can gate on.
- [libreyolo export](https://www.libreyolo.com/docs/cli/export): Export a checkpoint to a deployment format: every argument with its default, where the artifact lands, and the combinations the command refuses.
- [libreyolo label](https://www.libreyolo.com/docs/cli/label): Launch the local bounding-box annotation tool: arguments with defaults, the AI assist switch, and what binding to a network interface exposes.
- [libreyolo monitor](https://www.libreyolo.com/docs/cli/monitor): Serve a live dashboard for training runs: arguments with defaults, what the server reads from disk, and how one server covers many runs.
- [libreyolo predict](https://www.libreyolo.com/docs/cli/predict): Run inference from the command line: every argument, its default read from the CLI definition, and the flags that change what lands on stdout.
- [libreyolo profile](https://www.libreyolo.com/docs/cli/profile): Measure training and inference speed and read the result: every profile subcommand, its arguments and defaults, and what each lens reports.
- [libreyolo quantize](https://www.libreyolo.com/docs/cli/quantize): Quantize a checkpoint in PyTorch from the command line: recipes, calibration arguments, defaults, and the families each recipe accepts.
- [libreyolo train](https://www.libreyolo.com/docs/cli/train): Train a model from the command line: all 59 arguments with their defaults, how family defaults override them, and which arguments a family ignores.
- [libreyolo ui](https://www.libreyolo.com/docs/cli/ui): Launch the local inference web UI: bind address, port behavior, device selection, and how the command exits.
- [libreyolo utilities](https://www.libreyolo.com/docs/cli/utilities): The small LibreYOLO commands: version, checks, models, formats, cfg, info, metadata, enroll and compare, each with its arguments and defaults.
- [libreyolo val](https://www.libreyolo.com/docs/cli/val): Evaluate a checkpoint on a dataset split from the command line: every argument with its default, and the metric keys each task returns.

### Reference
- [Augmentation matrix](https://www.libreyolo.com/docs/reference/augmentation-matrix): Per-family augmentation knob support: the sixteen TrainConfig knobs, the three statuses, the six pipeline archetypes, and the knobs a family silently ignores.
- [Checkpoint schema](https://www.libreyolo.com/docs/reference/checkpoint-schema): The metadata every LibreYOLO .pt checkpoint carries: required keys, per-task additions, export runtime keys, quantized manifests and training fields.
- [CUDA graphs](https://www.libreyolo.com/docs/reference/cuda-graphs): Which families capture their forward at predict time and their forward and backward at train time, what the numbers are guaranteed to be, where a capture is split, and why an unsupported family raises.
- [Dataset formats](https://www.libreyolo.com/docs/reference/dataset-formats): The dataset-file contract per canonical task: YAML keys, folder layouts, label rows, mask and map conventions, and the loader that reads each one.
- [Ensemble API](https://www.libreyolo.com/docs/reference/ensemble-api): LibreEnsemble, ExternalDetector, and the three fusion ops in libreyolo.ops: weighted boxes fusion, its seeded variant, and class-aware NMS fusion.
- [Full export matrix](https://www.libreyolo.com/docs/reference/export-matrix): How LibreYOLO decides whether a family, task and format combination exports: the twelve formats, the three tiers, the fallback rules and the parity thresholds.
- [Kernels](https://www.libreyolo.com/docs/reference/kernels): How LibreYOLO selects accelerated implementations: the kernel registry under libreyolo/kernels, the optional Hugging Face Hub MS-deform-attn kernel, and the fused attention switch.
- [Model API](https://www.libreyolo.com/docs/reference/model-api): Every method on a loaded LibreYOLO model: predict, embed, track, val, train, export, save, quantize, info and the CUDA graph controls, with real defaults.
- [Open-vocabulary API](https://www.libreyolo.com/docs/reference/openvocab-api): The LibreOpenVocab factory, its four families and every alias, set_classes, the per-family conf defaults, and the text_threshold and iou rules.
- [Python API](https://www.libreyolo.com/docs/reference/python-api): The names LibreYOLO exports at package level: the five factories, the family classes, the Results payloads, backends, validators, trackers and data helpers.
- [Results types](https://www.libreyolo.com/docs/reference/results-types): Every payload a LibreYOLO Results object can carry, one slot per task shape: boxes, masks, keypoints, probs, obb, depth, ocr, embeddings and ten more.
- [Promptable segmentation API](https://www.libreyolo.com/docs/reference/sam-api): The LibreSAM factory, its size aliases, the point, box and concept-text prompt types, the encode-once set_image lifecycle, and what the tier does not support.
- [Settings](https://www.libreyolo.com/docs/reference/settings): Every environment variable LibreYOLO reads, the directories it writes to, the tokens it needs, and the toggles that change which code path runs.
- [Stability tiers](https://www.libreyolo.com/docs/reference/stability-tiers): The tier vocabulary LibreYOLO uses: the three export support tiers, the four API tiers, the six coverage groups, and what none of them promise.
- [Upstream checkpoints](https://www.libreyolo.com/docs/reference/upstream-checkpoints): How auto-conversion turns a released upstream checkpoint into a LibreYOLO v1.0 one: the layouts it unwraps, which families recognize what, and where it stops.
- [Vision-language API](https://www.libreyolo.com/docs/reference/vlm-api): The LibreVLM factory, every model alias, the sticky set_classes vocabulary, set_task, the chat escape hatch, and why confidence is a placeholder.

## Articles
Every article is also available as raw markdown: append .md to its URL (or .zh.md for the Chinese translation).
- [RF100-VL: benchmarking detectors on one hundred real datasets](https://www.libreyolo.com/articles/rf100vl-benchmark): We are running 15 detector configurations across all 100 RF100-VL datasets. Here is the benchmark explained, an interactive map of the datasets, and seven verified results.
- [LiteRT vs TensorFlow Lite: It Is a Rename, Not a New Format](https://www.libreyolo.com/articles/litert-vs-tensorflow-lite): Google renamed TensorFlow Lite to LiteRT in September 2024. Same runtime, same .tflite files, nothing breaks. Here is why they did it, what actually changed, and how to export a LibreYOLO model to it.
- [LibreYOLO mentions](https://www.libreyolo.com/articles/libreyolo-mentions): A running list of talks, blog posts, and community threads that mention LibreYOLO around the web, from CVPR 2026 to Hacker News and r/computervision.
- [Is YOLO Free for Commercial Use? YOLOv8, YOLO11 and YOLO26 Licenses](https://www.libreyolo.com/articles/yolo-commercial-license): YOLOv5, YOLOv8, YOLO11 and YOLO26 ship under AGPL-3.0, so they are not free for closed-source commercial use. Here is what the license actually requires, version by version, and the MIT-licensed alternative.
- [Best Ultralytics Alternatives in 2026](https://www.libreyolo.com/articles/best-ultralytics-alternatives): A practical guide to the best open-source alternatives to Ultralytics YOLO in 2026: their licenses, task coverage, and deployment limits, and where LibreYOLO, the most complete MIT-licensed YOLO library, fits.
- [LibreYOLO showed up at CVPR 2026](https://www.libreyolo.com/articles/libreyolo-at-cvpr-2026): At CVPR 2026 in Denver, a Jabra and IT University of Copenhagen team used LibreYOLOXs as the example model in their "Edge AI in Action" tutorial, running it on a Hailo-8L and on Snapdragon.
- [How to run RTMDet without mmdetection](https://www.libreyolo.com/articles/rtmdet-without-mmdetection): RTMDet is one of the fastest accurate detectors around. The official install is broken on any PyTorch released in the last two years. Here is the alternative.
- [How to run Depth Anything v2 with LibreYOLO](https://www.libreyolo.com/articles/simplest-way-to-run-depth-anything-v2): Depth Anything V2 gives you state-of-the-art monocular depth. Here is how to run it in two lines with LibreYOLO.
- [How to run YOLO-NAS with LibreYOLO](https://www.libreyolo.com/articles/yolo-nas-with-libreyolo): YOLO-NAS is one of the most accurate real-time detectors available but the original repo is not maintained. Here is how to run it with LibreYOLO.
- [How to run YOLOX with LibreYOLO](https://www.libreyolo.com/articles/yolox-with-libreyolo): YOLOX is Apache 2.0, commercially clean, and still competitive. The original repo is abandoned. Here is how to run it with LibreYOLO instead.

## Project links
- [LibreYOLO on GitHub](https://github.com/LibreYOLO/libreyolo): source code, issues, releases
- [libreyolo on PyPI](https://pypi.org/project/libreyolo/): official package
- [LibreYOLO on Hugging Face](https://huggingface.co/LibreYOLO): model weights and datasets
- [Live demo](https://huggingface.co/spaces/LibreYOLO/libreyolo-demo): try the models in the browser
- [Vision Analysis benchmarks](https://www.visionanalysis.org/): accuracy and speed leaderboard for every LibreYOLO model (its own index: https://www.visionanalysis.org/llms.txt)
- [r/LibreYOLO](https://www.reddit.com/r/LibreYOLO/): community subreddit