今後の展開

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from libreyolo import LibreYOLO, LibreYOLO9, LibreRFDETR
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# Start a task from scratch via the family class
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m = LibreYOLO9(None, size="t", task="classify", nb_classes=10)
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# Load a trained checkpoint via the unified factory (task auto-detected)
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m = LibreYOLO("LibreYOLO9t-obb.pt")

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9t-cls.pt")
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r = model.predict("cat.jpg")
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print(r.probs.top1)        # class id of the argmax
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print(r.probs.top1conf)    # its probability
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print(r.probs.top5)        # [id, id, id, id, id]
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print(model.names[r.probs.top1])  # human-readable label

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dataset/
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  train/
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    cat/   img001.jpg ...
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    dog/   img104.jpg ...
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  val/
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    cat/   ...
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    dog/   ...

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from libreyolo import LibreYOLO9
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model = LibreYOLO9(None, size="t", task="classify", nb_classes=10)
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result = model.train(
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    data="imagenette160",   # folder, .zip URL, or known name
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    epochs=10, batch=64, imgsz=224,
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    optimizer="adamw", lr0=1e-3,
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)
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# Validation reports metrics/accuracy_top1 and metrics/accuracy_top5

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9t-obb.pt")
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r = model.predict("aerial.jpg")
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for i in range(len(r.obb.cls)):
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    cx, cy, w, h, angle = r.obb.xywhr[i]  # angle in radians
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    corners = r.obb.xyxyxyxy[i]           # 4 (x, y) corner points
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    conf, cls = r.obb.conf[i], r.obb.cls[i]

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# class_id  x1 y1  x2 y2  x3 y3  x4 y4   (all normalized to [0, 1])
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0  0.51 0.32  0.66 0.38  0.62 0.55  0.47 0.49
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2  0.10 0.71  0.18 0.69  0.20 0.80  0.12 0.82

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from libreyolo import LibreYOLO9
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model = LibreYOLO9(None, size="t", task="obb")
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# Warm-start the backbone from a same-family detect checkpoint
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result = model.train(data="dota8.yaml", pretrained=True, epochs=100, imgsz=640)
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# CLI equivalent
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# libreyolo train model=LibreYOLO9t.pt data=dota8.yaml --task obb

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from libreyolo import LibreYOLO
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model = LibreYOLO("LibreYOLO9t-pose.pt")
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r = model.predict("athletes.jpg")
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kp = r.keypoints
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print(kp.xy.shape)   # (N, 17, 2) pixel coordinates
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print(kp.conf)       # (N, 17)    per-keypoint visibility / confidence
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print(kp.xyn)        # normalized coordinates
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print(r.boxes.xyxy)  # person boxes still come along

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path: coco8-pose
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train: images/train
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val: images/val
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nc: 1
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names:
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  0: person
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kpt_shape: [17, 3]
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flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]

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from libreyolo import LibreYOLO9
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# Warm-start from a detection checkpoint; the keypoint head is reinitialized
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model = LibreYOLO9("LibreYOLO9t.pt", size="t", task="pose")
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model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
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# Validation reports OKS-based AP via the pose validator

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from libreyolo import LibreYOLO
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# Auto-downloads from the LibreYOLO Hugging Face org
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model = LibreYOLO("LibreYOLO9P2s-visdrone.pt")
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# Evaluate/predict at 768 - the resolution it was trained at
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results = model.predict("aerial.jpg", imgsz=768, conf=0.25)

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from libreyolo import LibreYOLO9P2
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model = LibreYOLO9P2(None, size="s")
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model.train(
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    data="/abs/path/tiny_objects.yaml",
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    imgsz=768,                # resolution is the biggest lever for tiny objects
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    lr0=0.005,                # the family default 0.01 diverges on transfer init
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    mosaic_prob=0.0,          # mosaic tiling shrinks tiny objects below detectability
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    mixup_prob=0.0,
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    hsv_prob=1.0, flip_prob=0.5,
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    max_labels=600,           # dense aerial frames exceed the default 100-box cap
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    pretrained="LibreYOLO9s.pt",  # transfer init from stock YOLOv9
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    epochs=60,
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)

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from libreyolo import LibreYOLO
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model = LibreYOLO("rf-detr-nano.pth")   # sizes n, s, m, l
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result = model.train(
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    data="data.yaml",
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    lora=True,        # DoRA on the frozen DINOv2 backbone
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    epochs=100, batch_size=4, lr=1e-4,
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)
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# Resume: LoRA is auto-detected from the checkpoint, no need to repeat the flag
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model.train(data="data.yaml", resume=True)

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# CLI equivalent
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libreyolo train --model rf-detr-nano.pth --data data.yaml --lora