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pip install libreyolo

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git clone https://github.com/LibreYOLO/libreyolo.git
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cd libreyolo
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pip install -e .

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# ONNX export and inference
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pip install libreyolo[onnx]
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# or: pip install onnx onnxsim onnxruntime
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# RT-DETR compatibility extra (currently no extra packages)
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pip install libreyolo[rtdetr]
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# RF-DETR support
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pip install libreyolo[rfdetr]
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# or: pip install transformers
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# TensorRT export and inference (NVIDIA GPU)
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pip install libreyolo[tensorrt]
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# Installs TensorRT CUDA 12 Python packages on Linux/Windows.
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# Host driver/CUDA compatibility still matters.
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# OpenVINO export and inference (Intel CPU/GPU/VPU)
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pip install libreyolo[openvino]
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# INT8 export also needs: pip install nncf
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# NCNN export and inference
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pip install libreyolo[ncnn]
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# or: pip install pnnx ncnn
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# TFLite export + LiteRT runtime backend (Python 3.12+)
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pip install libreyolo[tflite]
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# "litert" is an alias extra: pip install libreyolo[litert]
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# Tracking API compatibility extra
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pip install libreyolo[tracking]
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# Tracking dependencies are part of the base install; Deep OC-SORT's ReID
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# embedder weights auto-download on first use.
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# CoreML export and inference (macOS only for runtime)
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pip install libreyolo[coreml]
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# or: pip install coremltools
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# L2CS gaze optional auto-download helper
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pip install libreyolo[gaze]
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# Promptable segmentation (LibreSAM: SAM-1, SAM-2, SAM 3, MobileSAM,
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# EdgeTAM, PicoSAM3)
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pip install libreyolo[sam]
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# Open-vocabulary detection (Grounding DINO, OWLv2, OmDet-Turbo, OV-DEIM)
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pip install libreyolo[openvocab]
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# LibreLabel AI assist (SAM click-to-mask)
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pip install libreyolo[label]
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# Zero-shot classification
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pip install libreyolo[clip]       # CLIP
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pip install libreyolo[siglip2]    # SigLIP2 tokenizer (SentencePiece)
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# Validation and training plots
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pip install libreyolo[plots]
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# SenseNova Vision preview
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pip install libreyolo[sensenova]
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# Converter-only dependencies for CLIP and SigLIP2 checkpoints
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pip install libreyolo[clip-convert]
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pip install libreyolo[siglip2-convert]
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# LoRA fine-tuning (peft)
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pip install libreyolo[lora]
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# Experiment loggers
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pip install libreyolo[tensorboard]   # or [mlflow], [wandb]
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# EoMT instance / panoptic segmentation
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pip install libreyolo[eomt]
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# Install every optional LibreYOLO extra
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pip install libreyolo[all]

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# ONNX environment
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uv venv .venv-onnx
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uv pip install --python .venv-onnx/bin/python -e '.[onnx]'
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# RT-DETR environment
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uv venv .venv-rtdetr
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uv pip install --python .venv-rtdetr/bin/python -e '.[rtdetr]'
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# Repeat with .[rfdetr], .[openvino], .[ncnn], .[coreml], .[gaze], .[tracking], or .[tensorrt] as needed

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from libreyolo import LibreYOLO, SAMPLE_IMAGE
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# Use the official checkpoint name and let the factory resolve the details
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model = LibreYOLO("LibreYOLO9c.pt")
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# Run on a single image (SAMPLE_IMAGE ships with the package)
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result = model(SAMPLE_IMAGE)
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print(f"Found {len(result)} objects")
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print(result.boxes.xyxy)   # bounding boxes (N, 4)
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print(result.boxes.conf)   # confidence scores (N,)
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print(result.boxes.cls)    # class IDs (N,)

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from libreyolo import LibreYOLO, SAMPLE_IMAGE
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# Same factory, same call shape - just point at an RF-DETR checkpoint
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model = LibreYOLO("LibreRFDETRs.pt")
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result = model(SAMPLE_IMAGE)
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print(f"Found {len(result)} objects")
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print(result.boxes.xyxy)

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result = model(SAMPLE_IMAGE, save=True)
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print(result.saved_path)   # e.g. runs/detect/predict/parkour.jpg

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results = model("images/", save=True, batch=4)
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for r in results:
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    print(f"{r.path}: {len(r)} detections")

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9c.pt")   # detection

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreRFDETRs.pt")           # detect (validated)
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# model = LibreYOLO("LibreRFDETRs-seg.pt")     # segment (validated)
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# model = LibreYOLO("LibreRFDETRx-pose.pt")    # pose  (research preview)
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# model = LibreYOLO("LibreRFDETRn-obb.pt")     # obb   (research preview)

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from libreyolo import LibreYOLO
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# Default: YOLO9 detection
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model = LibreYOLO("LibreYOLO9c.pt")
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# Flagship transformer: RF-DETR
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model = LibreYOLO("LibreRFDETRs.pt")
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model = LibreYOLO("LibreRFDETRs-seg.pt")        # validated segmentation
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# The task suffix selects the task
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model = LibreYOLO("LibreMobileNetV4s-cls.pt")   # classification (Apache, ImageNet-1k)
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model = LibreYOLO("LibreDINOv2n.pt")            # semantic segmentation
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model = LibreYOLO("LibreDepthAnythingV2s-depth.pt")  # monocular depth
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model = LibreYOLO("LibreFOMOs-point.pt")        # point localization (local weights)
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# New in v1.4.0
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model = LibreYOLO("LibreSegformerb2-sem.pt")    # semantic segmentation
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model = LibreYOLO("LibreEoMTb-panoptic.pt")     # panoptic segmentation
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model = LibreYOLO("LibreSwinIRm-restore.pt")    # 4x super-resolution
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model = LibreYOLO("LibreBiRefNetl-matte.pt")    # background removal
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model = LibreYOLO("LibrePPOCRt-ocr.pt")         # OCR (text detection + recognition)
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model = LibreYOLO("LibreZipDepthb-depth.pt")    # depth
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# Exported deployment formats
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model = LibreYOLO("model.onnx")                 # ONNX Runtime
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model = LibreYOLO("model.engine")               # TensorRT
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model = LibreYOLO("model.mlpackage")            # CoreML (macOS)
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model = LibreYOLO("model_openvino/")            # OpenVINO (directory)
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model = LibreYOLO("model_ncnn/")                # NCNN (directory)
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model = LibreYOLO("model.tflite")               # TFLite / LiteRT (new in v1.4.0)

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Libre<FAMILY><size>[-<task>].pt

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explicit task=  →  checkpoint["task"]  →  filename suffix  →  family default

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from libreyolo import LibreYOLO
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# 1. Filename suffix decides → segment
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model = LibreYOLO("LibreRFDETRs-seg.pt")
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# 2. Override regardless of filename
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model = LibreYOLO("custom_weights.pt", task="segment")
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# 3. Detection is implicit
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model = LibreYOLO("LibreYOLO9c.pt")  # task="detect"

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# Detection (implicit)
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LibreYOLO9c.pt
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LibreRFDETRs.pt
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LibreRTDETRr50.pt
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# Instance segmentation (-seg)
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LibreRFDETRs-seg.pt
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LibreECm-seg.pt
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# Semantic segmentation (-sem)
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LibreDINOv2n.pt          # semantic is DINOv2's default; -sem optional
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LibreSegformerb0-sem.pt  # SegFormer requires the -sem suffix
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# Panoptic segmentation (-panoptic)
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LibreEoMTs-panoptic.pt
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# Pose (-pose)
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LibreYOLONASn-pose.pt
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LibreECs-pose.pt
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LibreRFDETRx-pose.pt     # preview
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# Oriented boxes (-obb)
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LibreRFDETRn-obb.pt      # preview
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# Classification (-cls)
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LibreMobileNetV4s-cls.pt
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LibreConvNeXtt-cls.pt
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LibreEfficientNetV2b0-cls.pt
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# LibreDINOv2 classify checkpoints are not publicly shipped in v1.4.0
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LibreSigLIP2b16-cls.pt   # zero-shot
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# Depth (-depth)
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LibreDepthAnythingV2s-depth.pt
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LibreZipDepthb-depth.pt
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# Restoration / super-resolution (-restore)
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LibreNAFNetl-restore-sidd.pt
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LibreSwinIRm-restore.pt
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LibreRealESRGANx4-restore.pt
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# Background removal (-matte)
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LibreBiRefNetl-matte.pt
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# OCR (-ocr)
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LibrePPOCRt-ocr.pt
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# Point (-point)
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LibreFOMOs-point.pt
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# Gaze (-gaze optional; only task for L2CS)
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LibreL2CSr50.pt

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result = model("image.jpg")

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result = model(
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    "image.jpg",
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    conf=0.25,            # confidence threshold (default: 0.25)
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    iou=0.45,             # NMS IoU threshold (default: 0.45)
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    imgsz=640,            # input size override (default: model's native)
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    device="auto",        # "auto", "cpu", "mps", "0", "cuda:0", ...
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    classes=[0, 2, 5],    # filter to specific class IDs (default: all)
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    max_det=300,          # max detections per image (default: 300)
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    augment=False,        # test-time augmentation where implemented
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    save=True,            # save annotated image (default: False)
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    batch=4,              # directory batch size
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    stream=False,         # video only: yield frame results instead of a list
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    vid_stride=1,         # video only: process every N-th frame
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    show=False,           # video only: display annotated frames
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    tiling=False,         # large-image tiled detection
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    overlap_ratio=0.2,    # tile overlap ratio
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    output_path="out/",   # images: directory; video: final file path
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    color_format="auto",  # "auto", "rgb", or "bgr"
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    output_file_format="png",  # output format: "jpg", "png", "webp"
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)

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# File path (string or pathlib.Path)
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result = model("photo.jpg")
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result = model(Path("photo.jpg"))
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# URL
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result = model("https://example.com/image.jpg")
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result = model("s3://bucket/image.jpg")
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result = model("gs://bucket/image.jpg")
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# PIL Image
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from PIL import Image
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img = Image.open("photo.jpg")
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result = model(img)
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# NumPy array (HWC or CHW, RGB or BGR, uint8 or float32)
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import numpy as np
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arr = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
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result = model(arr)
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# OpenCV (BGR) - specify color_format
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import cv2
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frame = cv2.imread("photo.jpg")
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result = model(frame, color_format="bgr")
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# PyTorch tensor (CHW or NCHW)
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import torch
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tensor = torch.randn(3, 640, 640)
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result = model(tensor)
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# Raw bytes
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with open("photo.jpg", "rb") as f:
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    result = model(f.read())
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# BytesIO
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from io import BytesIO
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result = model(BytesIO(open("photo.jpg", "rb").read()))
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# Directory of images
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results = model("images/", batch=4)

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result = model("image.jpg")
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# Number of detections
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len(result)  # e.g., 5
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# Bounding boxes in xyxy format (x1, y1, x2, y2)
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result.boxes.xyxy        # tensor of shape (N, 4)
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# Bounding boxes in xywh format (center_x, center_y, width, height)
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result.boxes.xywh        # tensor of shape (N, 4)
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# Confidence scores
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result.boxes.conf        # tensor of shape (N,)
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# Class IDs
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result.boxes.cls         # tensor of shape (N,)
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# Combined data: [x1, y1, x2, y2, conf, cls]
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# Tracking adds a track_id column before conf/cls.
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result.boxes.data        # shape (N, 6), or (N, 7) when tracked
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# Metadata
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result.orig_shape        # (height, width) of original image
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result.path              # source file path (or None)
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result.names             # {0: "person", 1: "bicycle", ...}
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# Move to CPU / convert to numpy
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result_cpu = result.cpu()
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boxes_np = result.boxes.numpy()

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# Only detect people (class 0) and cars (class 2)
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result = model("image.jpg", classes=[0, 2])

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import numpy as np
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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9c.pt")
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frames = [
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    np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8),
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    np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8),
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    np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8),
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]
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results = model(frames, batch=4)   # list/tuple -> true batched inference
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for r in results:
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    print(len(r), r.boxes.xyxy.shape)

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meta = model.info(detailed=False, verbose=True)
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# meta -> {"family": ..., "size": ..., "task": ..., "params": ..., "imgsz": ..., "names": {...}, ...}

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result = model(
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    "large_aerial_image.jpg",
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    tiling=True,
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    overlap_ratio=0.2,   # 20% overlap between tiles (default)
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    save=True,
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)
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# Extra metadata on tiled results
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result.tiled           # True
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result.num_tiles       # number of tiles used
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result.saved_path      # output directory when save=True
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result.tiles_path      # directory containing per-tile crops
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result.grid_path       # grid visualization image

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9c.pt")
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results = model("clip.mp4", save=True)
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# Saved under runs/detect/predict*/clip.mp4

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for result in model("long_clip.mp4", stream=True):
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    print(f"frame {result.frame_idx}: {len(result)} detections")

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# Process every 2nd frame (halves compute and saved fps)
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results = model("clip.mp4", vid_stride=2, save=True)

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# Display annotated frames in an OpenCV window while processing
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results = model("clip.mp4", show=True)

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from libreyolo import LibreYOLO
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from libreyolo.utils.video import VideoSource, VideoWriter
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model = LibreYOLO("LibreYOLO9c.pt")
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with VideoSource("clip.mp4", vid_stride=1) as src, \
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     VideoWriter("out.mp4", fps=src.fps, width=src.width, height=src.height) as out:
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    for frame_bgr, frame_idx in src:
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        result = model(frame_bgr, color_format="bgr")
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        # ... draw, transform, etc.
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        out.write_frame(frame_bgr)

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pip install libreyolo[tracking]   # compatibility extra; tracking deps ship in base dev install

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9c.pt")
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for result in model.track(
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    "clip.mp4",
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    track_conf=0.25,
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    iou=0.45,
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    save=True,             # writes runs/track/<video_stem>.mp4 by default
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    vid_stride=1,
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):
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    print(result.frame_idx, result.track_id)

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from libreyolo import LibreYOLO, ByteTracker
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from libreyolo.utils.video import VideoSource
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model = LibreYOLO("LibreYOLO9c.pt")
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tracker = ByteTracker()
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with VideoSource("clip.mp4") as src:
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    for frame_bgr, frame_idx in src:
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        result = model(frame_bgr, color_format="bgr", conf=0.1)
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        tracked = tracker.update(result)
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        for i in range(len(tracked.boxes)):
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            track_id = int(tracked.boxes.id[i])
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            xyxy = tracked.boxes.xyxy[i].tolist()
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            cls = int(tracked.boxes.cls[i])
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            print(f"frame {frame_idx} - id {track_id} cls {cls} {xyxy}")

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from libreyolo import ByteTracker, TrackConfig
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cfg = TrackConfig(
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    track_high_thresh=0.25,           # first-stage match threshold
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    track_low_thresh=0.1,             # second-stage (low-conf recovery)
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    new_track_thresh=0.25,            # minimum conf to start a new track
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    match_thresh=0.8,                 # IoU cost cutoff (stage 1)
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    match_thresh_low=0.5,             # IoU cost cutoff (stage 2)
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    match_thresh_unconfirmed=0.7,     # IoU cost cutoff for unconfirmed tracks
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    track_buffer=30,                  # frames to keep lost tracks before removal
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    frame_rate=30,                    # scales track_buffer
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    fuse_score=True,                  # multiply IoU by detection score
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    minimum_consecutive_frames=1,     # frames to confirm a new track
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)
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tracker = ByteTracker(config=cfg)

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tracker.reset()   # clears tracked / lost / removed lists and the ID counter

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9c.pt")
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for result in model.track(
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    "clip.mp4",
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    tracker="ocsort",      # "bytetrack" (default) or "ocsort"
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    track_conf=0.25,       # maps to OC-SORT det_thresh
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    iou=0.45,
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    save=True,
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):
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    print(result.frame_idx, result.track_id)

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from libreyolo import LibreYOLO, OCSortConfig
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cfg = OCSortConfig(
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    det_thresh=0.25,     # boxes above this drive association and spawn new tracks
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    max_age=30,          # frames a track survives without an observation
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    min_hits=3,          # consecutive hits before a track is reported
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    iou_threshold=0.3,   # minimum IoU for a valid association
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    delta_t=3,           # frame span used to estimate velocity direction
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    inertia=0.2,         # weight of the velocity-direction (momentum) term
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    use_byte=False,      # enable the BYTE low-score recovery pass
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)
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model = LibreYOLO("LibreYOLO9c.pt")
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for result in model.track("clip.mp4", tracker_config=cfg, save=True):
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    print(result.frame_idx, result.track_id)

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from libreyolo import LibreYOLO, BoTSortConfig
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model = LibreYOLO("LibreYOLO9c.pt")
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# Select by name with defaults
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for result in model.track("drone.mp4", tracker="botsort", save=True):
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    print(result.frame_idx, result.track_id)
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# Or configure it: a config instance selects the tracker by type
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cfg = BoTSortConfig(
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    track_high_thresh=0.25,
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    track_buffer=30,
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    enable_cmc=True,              # camera-motion compensation on (default)
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    cmc_method="sparseOptFlow",   # the shipped CMC estimator
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    cmc_downscale=2,              # estimate flow at half resolution
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)
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for result in model.track("drone.mp4", tracker_config=cfg, save=True):
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    print(result.frame_idx, result.track_id)

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from libreyolo import LibreYOLO
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from libreyolo.tracking import DeepOCSortConfig
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model = LibreYOLO("LibreYOLO9c.pt")
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# Defaults: OSNet-AIN embedder, auto-downloaded
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for result in model.track("mall.mp4", tracker="deepocsort", save=True):
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    print(result.frame_idx, result.track_id)
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# Tune the appearance term, or plug in your own embedder
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cfg = DeepOCSortConfig(
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    det_thresh=0.25,
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    embedder="osnet_ain_x0_25",   # or a callable: (frame, boxes_xyxy) -> (N, D) features
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    w_association_emb=0.75,       # weight of appearance vs motion in matching
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    alpha_fixed_emb=0.95,         # EMA smoothing of per-track embeddings
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)
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for result in model.track("mall.mp4", tracker_config=cfg, save=True):
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    print(result.frame_idx, result.track_id)

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from libreyolo import LibreEnsemble
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# 加权框融合（WBF，默认），只保留两个模型都找到的框
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ens = LibreEnsemble(["LibreYOLO9s.pt", "LibreRFDETRs.pt"], min_votes=2)
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result = ens("image.jpg", conf=0.25)
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print(result.boxes.xyxy)
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print(result.names)     # 统一后的（并集）类别表

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ens = LibreEnsemble(
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    ["LibreYOLO9s.pt", "LibreRFDETRs.pt"],
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    weights=[1.0, 1.4],     # 让融合结果更偏向第二个成员
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    fusion="wbf",           # "wbf" | "wbf_seeded" | "nms" | 自定义可调用对象
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    fusion_iou=0.55,        # 用于聚类的 IoU，不是成员的 NMS
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    min_votes=1,
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)
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result = ens("image.jpg", conf=[0.25, 0.4])   # 每个成员单独的置信度阈值