─░├žeri─če ge├ž

Referans i├žin ultralytics/utils/metrics.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/utils/metrics .py adresinde mevcuttur. Bir sorun tespit ederseniz l├╝tfen bir ├çekme ─░ste─či ­čŤá´ŞĆ ile katk─▒da bulunarak d├╝zeltilmesine yard─▒mc─▒ olun. Te┼čekk├╝rler ­čÖĆ!



ultralytics.utils.metrics.ConfusionMatrix

Nesne alg─▒lama ve s─▒n─▒fland─▒rma g├Ârevleri i├žin bir kar─▒┼č─▒kl─▒k matrisini hesaplamaya ve g├╝ncellemeye y├Ânelik bir s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
task str

G├Ârev t├╝r├╝, 'tespit' ya da 's─▒n─▒fland─▒rma'.

matrix ndarray

Boyutlar─▒ g├Âreve ba─čl─▒ olan kar─▒┼č─▒kl─▒k matrisi.

nc int

S─▒n─▒f say─▒s─▒.

conf float

Tespitler i├žin g├╝ven e┼či─či.

iou_thres float

Union e┼či─či ├╝zerindeki kav┼čak.

Kaynak kodu ultralytics/utils/metrics.py
class ConfusionMatrix:
    """
    A class for calculating and updating a confusion matrix for object detection and classification tasks.

    Attributes:
        task (str): The type of task, either 'detect' or 'classify'.
        matrix (np.ndarray): The confusion matrix, with dimensions depending on the task.
        nc (int): The number of classes.
        conf (float): The confidence threshold for detections.
        iou_thres (float): The Intersection over Union threshold.
    """

    def __init__(self, nc, conf=0.25, iou_thres=0.45, task="detect"):
        """Initialize attributes for the YOLO model."""
        self.task = task
        self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == "detect" else np.zeros((nc, nc))
        self.nc = nc  # number of classes
        self.conf = 0.25 if conf in {None, 0.001} else conf  # apply 0.25 if default val conf is passed
        self.iou_thres = iou_thres

    def process_cls_preds(self, preds, targets):
        """
        Update confusion matrix for classification task.

        Args:
            preds (Array[N, min(nc,5)]): Predicted class labels.
            targets (Array[N, 1]): Ground truth class labels.
        """
        preds, targets = torch.cat(preds)[:, 0], torch.cat(targets)
        for p, t in zip(preds.cpu().numpy(), targets.cpu().numpy()):
            self.matrix[p][t] += 1

    def process_batch(self, detections, gt_bboxes, gt_cls):
        """
        Update confusion matrix for object detection task.

        Args:
            detections (Array[N, 6] | Array[N, 7]): Detected bounding boxes and their associated information.
                                      Each row should contain (x1, y1, x2, y2, conf, class)
                                      or with an additional element `angle` when it's obb.
            gt_bboxes (Array[M, 4]| Array[N, 5]): Ground truth bounding boxes with xyxy/xyxyr format.
            gt_cls (Array[M]): The class labels.
        """
        if gt_cls.shape[0] == 0:  # Check if labels is empty
            if detections is not None:
                detections = detections[detections[:, 4] > self.conf]
                detection_classes = detections[:, 5].int()
                for dc in detection_classes:
                    self.matrix[dc, self.nc] += 1  # false positives
            return
        if detections is None:
            gt_classes = gt_cls.int()
            for gc in gt_classes:
                self.matrix[self.nc, gc] += 1  # background FN
            return

        detections = detections[detections[:, 4] > self.conf]
        gt_classes = gt_cls.int()
        detection_classes = detections[:, 5].int()
        is_obb = detections.shape[1] == 7 and gt_bboxes.shape[1] == 5  # with additional `angle` dimension
        iou = (
            batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))
            if is_obb
            else box_iou(gt_bboxes, detections[:, :4])
        )

        x = torch.where(iou > self.iou_thres)
        if x[0].shape[0]:
            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
            if x[0].shape[0] > 1:
                matches = matches[matches[:, 2].argsort()[::-1]]
                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
                matches = matches[matches[:, 2].argsort()[::-1]]
                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
        else:
            matches = np.zeros((0, 3))

        n = matches.shape[0] > 0
        m0, m1, _ = matches.transpose().astype(int)
        for i, gc in enumerate(gt_classes):
            j = m0 == i
            if n and sum(j) == 1:
                self.matrix[detection_classes[m1[j]], gc] += 1  # correct
            else:
                self.matrix[self.nc, gc] += 1  # true background

        if n:
            for i, dc in enumerate(detection_classes):
                if not any(m1 == i):
                    self.matrix[dc, self.nc] += 1  # predicted background

    def matrix(self):
        """Returns the confusion matrix."""
        return self.matrix

    def tp_fp(self):
        """Returns true positives and false positives."""
        tp = self.matrix.diagonal()  # true positives
        fp = self.matrix.sum(1) - tp  # false positives
        # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
        return (tp[:-1], fp[:-1]) if self.task == "detect" else (tp, fp)  # remove background class if task=detect

    @TryExcept("WARNING ÔÜá´ŞĆ ConfusionMatrix plot failure")
    @plt_settings()
    def plot(self, normalize=True, save_dir="", names=(), on_plot=None):
        """
        Plot the confusion matrix using seaborn and save it to a file.

        Args:
            normalize (bool): Whether to normalize the confusion matrix.
            save_dir (str): Directory where the plot will be saved.
            names (tuple): Names of classes, used as labels on the plot.
            on_plot (func): An optional callback to pass plots path and data when they are rendered.
        """
        import seaborn  # scope for faster 'import ultralytics'

        array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1)  # normalize columns
        array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)

        fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
        nc, nn = self.nc, len(names)  # number of classes, names
        seaborn.set_theme(font_scale=1.0 if nc < 50 else 0.8)  # for label size
        labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
        ticklabels = (list(names) + ["background"]) if labels else "auto"
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
            seaborn.heatmap(
                array,
                ax=ax,
                annot=nc < 30,
                annot_kws={"size": 8},
                cmap="Blues",
                fmt=".2f" if normalize else ".0f",
                square=True,
                vmin=0.0,
                xticklabels=ticklabels,
                yticklabels=ticklabels,
            ).set_facecolor((1, 1, 1))
        title = "Confusion Matrix" + " Normalized" * normalize
        ax.set_xlabel("True")
        ax.set_ylabel("Predicted")
        ax.set_title(title)
        plot_fname = Path(save_dir) / f'{title.lower().replace(" ", "_")}.png'
        fig.savefig(plot_fname, dpi=250)
        plt.close(fig)
        if on_plot:
            on_plot(plot_fname)

    def print(self):
        """Print the confusion matrix to the console."""
        for i in range(self.nc + 1):
            LOGGER.info(" ".join(map(str, self.matrix[i])))

__init__(nc, conf=0.25, iou_thres=0.45, task='detect')

YOLO modeli i├žin ├Âznitelikleri ba┼člat─▒n.

Kaynak kodu ultralytics/utils/metrics.py
def __init__(self, nc, conf=0.25, iou_thres=0.45, task="detect"):
    """Initialize attributes for the YOLO model."""
    self.task = task
    self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == "detect" else np.zeros((nc, nc))
    self.nc = nc  # number of classes
    self.conf = 0.25 if conf in {None, 0.001} else conf  # apply 0.25 if default val conf is passed
    self.iou_thres = iou_thres

matrix()

Kar─▒┼č─▒kl─▒k matrisini d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def matrix(self):
    """Returns the confusion matrix."""
    return self.matrix

plot(normalize=True, save_dir='', names=(), on_plot=None)

Kar─▒┼č─▒kl─▒k matrisini seaborn kullanarak ├žizin ve bir dosyaya kaydedin.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
normalize bool

Kar─▒┼č─▒kl─▒k matrisinin normalle┼čtirilip normalle┼čtirilmeyece─či.

True
save_dir str

├çizimin kaydedilece─či dizin.

''
names tuple

Çizimde etiket olarak kullanılan sınıfların adları.

()
on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama.

None
Kaynak kodu ultralytics/utils/metrics.py
@TryExcept("WARNING ÔÜá´ŞĆ ConfusionMatrix plot failure")
@plt_settings()
def plot(self, normalize=True, save_dir="", names=(), on_plot=None):
    """
    Plot the confusion matrix using seaborn and save it to a file.

    Args:
        normalize (bool): Whether to normalize the confusion matrix.
        save_dir (str): Directory where the plot will be saved.
        names (tuple): Names of classes, used as labels on the plot.
        on_plot (func): An optional callback to pass plots path and data when they are rendered.
    """
    import seaborn  # scope for faster 'import ultralytics'

    array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1)  # normalize columns
    array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)

    fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
    nc, nn = self.nc, len(names)  # number of classes, names
    seaborn.set_theme(font_scale=1.0 if nc < 50 else 0.8)  # for label size
    labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
    ticklabels = (list(names) + ["background"]) if labels else "auto"
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
        seaborn.heatmap(
            array,
            ax=ax,
            annot=nc < 30,
            annot_kws={"size": 8},
            cmap="Blues",
            fmt=".2f" if normalize else ".0f",
            square=True,
            vmin=0.0,
            xticklabels=ticklabels,
            yticklabels=ticklabels,
        ).set_facecolor((1, 1, 1))
    title = "Confusion Matrix" + " Normalized" * normalize
    ax.set_xlabel("True")
    ax.set_ylabel("Predicted")
    ax.set_title(title)
    plot_fname = Path(save_dir) / f'{title.lower().replace(" ", "_")}.png'
    fig.savefig(plot_fname, dpi=250)
    plt.close(fig)
    if on_plot:
        on_plot(plot_fname)

print()

Kar─▒┼č─▒kl─▒k matrisini konsola yazd─▒r─▒n.

Kaynak kodu ultralytics/utils/metrics.py
def print(self):
    """Print the confusion matrix to the console."""
    for i in range(self.nc + 1):
        LOGGER.info(" ".join(map(str, self.matrix[i])))

process_batch(detections, gt_bboxes, gt_cls)

Nesne alg─▒lama g├Ârevi i├žin kar─▒┼č─▒kl─▒k matrisini g├╝ncelleyin.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
detections Array[N, 6] | Array[N, 7]

Tespit edilen s─▒n─▒rlay─▒c─▒ kutular ve bunlarla ili┼čkili bilgiler. Her sat─▒r (x1, y1, x2, y2, conf, class) i├žermelidir veya ek bir ├Â─če ile angle obb oldu─čunda.

gerekli
gt_bboxes Array[M, 4] | Array[N, 5]

xyxy/xyxyr format─▒nda zemin ger├že─či s─▒n─▒rlay─▒c─▒ kutular─▒.

gerekli
gt_cls Array[M]

S─▒n─▒f etiketleri.

gerekli
Kaynak kodu ultralytics/utils/metrics.py
def process_batch(self, detections, gt_bboxes, gt_cls):
    """
    Update confusion matrix for object detection task.

    Args:
        detections (Array[N, 6] | Array[N, 7]): Detected bounding boxes and their associated information.
                                  Each row should contain (x1, y1, x2, y2, conf, class)
                                  or with an additional element `angle` when it's obb.
        gt_bboxes (Array[M, 4]| Array[N, 5]): Ground truth bounding boxes with xyxy/xyxyr format.
        gt_cls (Array[M]): The class labels.
    """
    if gt_cls.shape[0] == 0:  # Check if labels is empty
        if detections is not None:
            detections = detections[detections[:, 4] > self.conf]
            detection_classes = detections[:, 5].int()
            for dc in detection_classes:
                self.matrix[dc, self.nc] += 1  # false positives
        return
    if detections is None:
        gt_classes = gt_cls.int()
        for gc in gt_classes:
            self.matrix[self.nc, gc] += 1  # background FN
        return

    detections = detections[detections[:, 4] > self.conf]
    gt_classes = gt_cls.int()
    detection_classes = detections[:, 5].int()
    is_obb = detections.shape[1] == 7 and gt_bboxes.shape[1] == 5  # with additional `angle` dimension
    iou = (
        batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))
        if is_obb
        else box_iou(gt_bboxes, detections[:, :4])
    )

    x = torch.where(iou > self.iou_thres)
    if x[0].shape[0]:
        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
        if x[0].shape[0] > 1:
            matches = matches[matches[:, 2].argsort()[::-1]]
            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
            matches = matches[matches[:, 2].argsort()[::-1]]
            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
    else:
        matches = np.zeros((0, 3))

    n = matches.shape[0] > 0
    m0, m1, _ = matches.transpose().astype(int)
    for i, gc in enumerate(gt_classes):
        j = m0 == i
        if n and sum(j) == 1:
            self.matrix[detection_classes[m1[j]], gc] += 1  # correct
        else:
            self.matrix[self.nc, gc] += 1  # true background

    if n:
        for i, dc in enumerate(detection_classes):
            if not any(m1 == i):
                self.matrix[dc, self.nc] += 1  # predicted background

process_cls_preds(preds, targets)

S─▒n─▒fland─▒rma g├Ârevi i├žin kar─▒┼č─▒kl─▒k matrisini g├╝ncelleyin.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
preds Array[N, min(nc, 5)]

Tahmin edilen s─▒n─▒f etiketleri.

gerekli
targets Array[N, 1]

Temel ger├žek s─▒n─▒f etiketleri.

gerekli
Kaynak kodu ultralytics/utils/metrics.py
def process_cls_preds(self, preds, targets):
    """
    Update confusion matrix for classification task.

    Args:
        preds (Array[N, min(nc,5)]): Predicted class labels.
        targets (Array[N, 1]): Ground truth class labels.
    """
    preds, targets = torch.cat(preds)[:, 0], torch.cat(targets)
    for p, t in zip(preds.cpu().numpy(), targets.cpu().numpy()):
        self.matrix[p][t] += 1

tp_fp()

Do─čru pozitifleri ve yanl─▒┼č pozitifleri d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def tp_fp(self):
    """Returns true positives and false positives."""
    tp = self.matrix.diagonal()  # true positives
    fp = self.matrix.sum(1) - tp  # false positives
    # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
    return (tp[:-1], fp[:-1]) if self.task == "detect" else (tp, fp)  # remove background class if task=detect



ultralytics.utils.metrics.Metric

├ťsler: SimpleClass

YOLOv8 modeli i├žin de─čerlendirme metriklerini hesaplamaya yarayan s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
p list

Her s─▒n─▒f i├žin hassasiyet. ┼×ekil: (nc,).

r list

Her s─▒n─▒f i├žin hat─▒rlama. ┼×ekil: (nc,).

f1 list

Her s─▒n─▒f i├žin F1 puan─▒. ┼×ekil: (nc,).

all_ap list

T├╝m s─▒n─▒flar ve t├╝m IoU e┼čikleri i├žin AP puanlar─▒. ┼×ekil: (nc, 10).

ap_class_index list

Her AP puan─▒ i├žin s─▒n─▒f indeksi. ┼×ekil: (nc,).

nc int

S─▒n─▒f say─▒s─▒.

Y├Ântemler:

─░sim A├ž─▒klama
ap50

T├╝m s─▒n─▒flar i├žin 0,5 IoU e┼či─činde AP. Geri d├Ân├╝┼čler: AP puanlar─▒n─▒n listesi. ┼×ekil: (nc,) veya [].

ap

T├╝m s─▒n─▒flar i├žin 0,5 ila 0,95 IoU e┼čiklerinde AP. Geri d├Ân├╝┼čler: AP puanlar─▒n─▒n listesi. ┼×ekil: (nc,) veya [].

mp

T├╝m s─▒n─▒flar─▒n ortalama hassasiyeti. Geri d├Ân├╝┼čler: Float.

mr

T├╝m s─▒n─▒flar─▒n ortalama geri ├ža─č─▒rmas─▒. Geri d├Ân├╝┼čler: Float.

map50

T├╝m s─▒n─▒flar i├žin 0,5 IoU e┼či─činde ortalama AP. Geri d├Ân├╝┼čler: Float.

map75

T├╝m s─▒n─▒flar i├žin 0,75 IoU e┼či─činde ortalama AP. Geri d├Ân├╝┼čler: Float.

map

T├╝m s─▒n─▒flar i├žin 0,5 ila 0,95 IoU e┼čiklerinde ortalama AP. Geri d├Ân├╝┼čler: Float.

mean_results

Sonu├žlar─▒n ortalamas─▒, mp, mr, map50, map d├Ând├╝r├╝r.

class_result

S─▒n─▒f fark─▒ndal─▒ sonu├ž, p[i], r[i], ap50[i], ap[i] d├Ând├╝r├╝r.

maps

her s─▒n─▒f─▒n mAP'si. D├Ând├╝r├╝r: mAP puanlar─▒ dizisi, ┼čekil: (nc,).

fitness

Metriklerin a─č─▒rl─▒kl─▒ bir kombinasyonu olarak model uygunlu─ču. Geri d├Ân├╝┼čler: Float.

update

Metrik ├Âznitelikleri yeni de─čerlendirme sonu├žlar─▒yla g├╝ncelleyin.

Kaynak kodu ultralytics/utils/metrics.py
class Metric(SimpleClass):
    """
    Class for computing evaluation metrics for YOLOv8 model.

    Attributes:
        p (list): Precision for each class. Shape: (nc,).
        r (list): Recall for each class. Shape: (nc,).
        f1 (list): F1 score for each class. Shape: (nc,).
        all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
        ap_class_index (list): Index of class for each AP score. Shape: (nc,).
        nc (int): Number of classes.

    Methods:
        ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
        ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
        mp(): Mean precision of all classes. Returns: Float.
        mr(): Mean recall of all classes. Returns: Float.
        map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float.
        map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float.
        map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float.
        mean_results(): Mean of results, returns mp, mr, map50, map.
        class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i].
        maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,).
        fitness(): Model fitness as a weighted combination of metrics. Returns: Float.
        update(results): Update metric attributes with new evaluation results.
    """

    def __init__(self) -> None:
        """Initializes a Metric instance for computing evaluation metrics for the YOLOv8 model."""
        self.p = []  # (nc, )
        self.r = []  # (nc, )
        self.f1 = []  # (nc, )
        self.all_ap = []  # (nc, 10)
        self.ap_class_index = []  # (nc, )
        self.nc = 0

    @property
    def ap50(self):
        """
        Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes.

        Returns:
            (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available.
        """
        return self.all_ap[:, 0] if len(self.all_ap) else []

    @property
    def ap(self):
        """
        Returns the Average Precision (AP) at an IoU threshold of 0.5-0.95 for all classes.

        Returns:
            (np.ndarray, list): Array of shape (nc,) with AP50-95 values per class, or an empty list if not available.
        """
        return self.all_ap.mean(1) if len(self.all_ap) else []

    @property
    def mp(self):
        """
        Returns the Mean Precision of all classes.

        Returns:
            (float): The mean precision of all classes.
        """
        return self.p.mean() if len(self.p) else 0.0

    @property
    def mr(self):
        """
        Returns the Mean Recall of all classes.

        Returns:
            (float): The mean recall of all classes.
        """
        return self.r.mean() if len(self.r) else 0.0

    @property
    def map50(self):
        """
        Returns the mean Average Precision (mAP) at an IoU threshold of 0.5.

        Returns:
            (float): The mAP at an IoU threshold of 0.5.
        """
        return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0

    @property
    def map75(self):
        """
        Returns the mean Average Precision (mAP) at an IoU threshold of 0.75.

        Returns:
            (float): The mAP at an IoU threshold of 0.75.
        """
        return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0

    @property
    def map(self):
        """
        Returns the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05.

        Returns:
            (float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05.
        """
        return self.all_ap.mean() if len(self.all_ap) else 0.0

    def mean_results(self):
        """Mean of results, return mp, mr, map50, map."""
        return [self.mp, self.mr, self.map50, self.map]

    def class_result(self, i):
        """Class-aware result, return p[i], r[i], ap50[i], ap[i]."""
        return self.p[i], self.r[i], self.ap50[i], self.ap[i]

    @property
    def maps(self):
        """MAP of each class."""
        maps = np.zeros(self.nc) + self.map
        for i, c in enumerate(self.ap_class_index):
            maps[c] = self.ap[i]
        return maps

    def fitness(self):
        """Model fitness as a weighted combination of metrics."""
        w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
        return (np.array(self.mean_results()) * w).sum()

    def update(self, results):
        """
        Updates the evaluation metrics of the model with a new set of results.

        Args:
            results (tuple): A tuple containing the following evaluation metrics:
                - p (list): Precision for each class. Shape: (nc,).
                - r (list): Recall for each class. Shape: (nc,).
                - f1 (list): F1 score for each class. Shape: (nc,).
                - all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
                - ap_class_index (list): Index of class for each AP score. Shape: (nc,).

        Side Effects:
            Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based
            on the values provided in the `results` tuple.
        """
        (
            self.p,
            self.r,
            self.f1,
            self.all_ap,
            self.ap_class_index,
            self.p_curve,
            self.r_curve,
            self.f1_curve,
            self.px,
            self.prec_values,
        ) = results

    @property
    def curves(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return []

    @property
    def curves_results(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return [
            [self.px, self.prec_values, "Recall", "Precision"],
            [self.px, self.f1_curve, "Confidence", "F1"],
            [self.px, self.p_curve, "Confidence", "Precision"],
            [self.px, self.r_curve, "Confidence", "Recall"],
        ]

ap property

T├╝m s─▒n─▒flar i├žin 0,5-0,95 IoU e┼či─činde Ortalama Hassasiyeti (AP) d├Ând├╝r├╝r.

─░ade:

Tip A├ž─▒klama
(ndarray, list)

S─▒n─▒f ba┼č─▒na AP50-95 de─čerlerine sahip ┼čekil (nc,) dizisi veya mevcut de─čilse bo┼č bir liste.

ap50 property

T├╝m s─▒n─▒flar i├žin 0,5 IoU e┼či─činde Ortalama Kesinlik (AP) de─čerini d├Ând├╝r├╝r.

─░ade:

Tip A├ž─▒klama
(ndarray, list)

S─▒n─▒f ba┼č─▒na AP50 de─čerlerini i├žeren ┼čekil (nc,) dizisi veya mevcut de─čilse bo┼č bir liste.

curves property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

curves_results property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

map property

Ortalama Ortalama Hassasiyeti (mAP) 0,05'lik ad─▒mlarla 0,5 - 0,95 IoU e┼čikleri ├╝zerinden d├Ând├╝r├╝r.

─░ade:

Tip A├ž─▒klama
float

0,05'lik ad─▒mlarla 0,5 - 0,95 IoU e┼čikleri ├╝zerinden mAP.

map50 property

Ortalama Ortalama Hassasiyeti (mAP) 0,5 IoU e┼či─činde d├Ând├╝r├╝r.

─░ade:

Tip A├ž─▒klama
float

IoU e┼či─či 0,5'te mAP.

map75 property

Ortalama Ortalama Hassasiyeti (mAP) 0,75 IoU e┼či─činde d├Ând├╝r├╝r.

─░ade:

Tip A├ž─▒klama
float

IoU e┼či─či 0,75 olan mAP.

maps property

Her s─▒n─▒f─▒n haritas─▒.

mp property

T├╝m s─▒n─▒flar─▒n Ortalama Hassasiyetini d├Ând├╝r├╝r.

─░ade:

Tip A├ž─▒klama
float

T├╝m s─▒n─▒flar─▒n ortalama hassasiyeti.

mr property

T├╝m s─▒n─▒flar─▒n Ortalama Geri ├ça─č─▒rma de─čerini verir.

─░ade:

Tip A├ž─▒klama
float

T├╝m s─▒n─▒flar─▒n ortalama hat─▒rlama say─▒s─▒.

__init__()

YOLOv8 modeli i├žin de─čerlendirme metriklerini hesaplamak ├╝zere bir Metrik ├Ârne─či ba┼člat─▒r.

Kaynak kodu ultralytics/utils/metrics.py
def __init__(self) -> None:
    """Initializes a Metric instance for computing evaluation metrics for the YOLOv8 model."""
    self.p = []  # (nc, )
    self.r = []  # (nc, )
    self.f1 = []  # (nc, )
    self.all_ap = []  # (nc, 10)
    self.ap_class_index = []  # (nc, )
    self.nc = 0

class_result(i)

S─▒n─▒f fark─▒ndal─▒ sonu├ž, p[i], r[i], ap50[i], ap[i] d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def class_result(self, i):
    """Class-aware result, return p[i], r[i], ap50[i], ap[i]."""
    return self.p[i], self.r[i], self.ap50[i], self.ap[i]

fitness()

Uygunlu─ču, metriklerin a─č─▒rl─▒kl─▒ bir kombinasyonu olarak modelleyin.

Kaynak kodu ultralytics/utils/metrics.py
def fitness(self):
    """Model fitness as a weighted combination of metrics."""
    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
    return (np.array(self.mean_results()) * w).sum()

mean_results()

Sonu├žlar─▒n ortalamas─▒, mp, mr, map50, map d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def mean_results(self):
    """Mean of results, return mp, mr, map50, map."""
    return [self.mp, self.mr, self.map50, self.map]

update(results)

Modelin de─čerlendirme metriklerini yeni bir sonu├ž k├╝mesiyle g├╝nceller.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
results tuple

A┼ča─č─▒daki de─čerlendirme metriklerini i├žeren bir tuple: - p (liste): Her s─▒n─▒f i├žin hassasiyet. ┼×ekil: (nc,). - r (liste): Her s─▒n─▒f i├žin geri ├ža─č─▒rma. ┼×ekil: (nc,). - f1 (liste): Her s─▒n─▒f i├žin F1 puan─▒. ┼×ekil: (nc,). - all_ap (liste): T├╝m s─▒n─▒flar ve t├╝m IoU e┼čikleri i├žin AP puanlar─▒. ┼×ekil: (nc, 10). - ap_class_index (liste): Her AP puan─▒ i├žin s─▒n─▒f indeksi. ┼×ekil: (nc,).

gerekli
Yan Etkiler

S─▒n─▒f ├Âzniteliklerini g├╝nceller self.p, self.r, self.f1, self.all_apve self.ap_class_index temelli i├žinde sa─članan de─čerler ├╝zerinde results tuple.

Kaynak kodu ultralytics/utils/metrics.py
def update(self, results):
    """
    Updates the evaluation metrics of the model with a new set of results.

    Args:
        results (tuple): A tuple containing the following evaluation metrics:
            - p (list): Precision for each class. Shape: (nc,).
            - r (list): Recall for each class. Shape: (nc,).
            - f1 (list): F1 score for each class. Shape: (nc,).
            - all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
            - ap_class_index (list): Index of class for each AP score. Shape: (nc,).

    Side Effects:
        Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based
        on the values provided in the `results` tuple.
    """
    (
        self.p,
        self.r,
        self.f1,
        self.all_ap,
        self.ap_class_index,
        self.p_curve,
        self.r_curve,
        self.f1_curve,
        self.px,
        self.prec_values,
    ) = results



ultralytics.utils.metrics.DetMetrics

├ťsler: SimpleClass

Bu s─▒n─▒f, kesinlik, geri ├ža─č─▒rma ve ortalama ortalama kesinlik gibi alg─▒lama ├Âl├ž├╝mlerini hesaplamak i├žin kullan─▒lan bir yard─▒mc─▒ s─▒n─▒ft─▒r (mAP) bir nesne alg─▒lama modeli.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
save_dir Path

├ç─▒kt─▒ ├žizimlerinin kaydedilece─či dizine giden yol. Varsay─▒lan de─čer ge├žerli dizindir.

Path('.')
plot bool

Her s─▒n─▒f i├žin hassasiyet-hat─▒rlama e─črilerinin ├žizilip ├žizilmeyece─čini belirten bir bayrak. Varsay─▒lan de─čer False'dir.

False
on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama. Varsay─▒lan de─čer Yok'tur.

None
names tuple of str

S─▒n─▒flar─▒n adlar─▒n─▒ temsil eden dizelerden olu┼čan bir tuple. Varsay─▒lan de─čer bo┼č bir tuple'd─▒r.

()

Nitelikler:

─░sim Tip A├ž─▒klama
save_dir Path

├ç─▒kt─▒ grafiklerinin kaydedilece─či dizine giden yol.

plot bool

Her s─▒n─▒f i├žin kesinlik-geri ├ža─č─▒rma e─črilerinin ├žizilip ├žizilmeyece─čini belirten bir bayrak.

on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama.

names tuple of str

S─▒n─▒flar─▒n adlar─▒n─▒ temsil eden bir dizi k├╝mesi.

box Metric

Alg─▒lama metriklerinin sonu├žlar─▒n─▒ saklamak i├žin Metrik s─▒n─▒f─▒n─▒n bir ├Ârne─či.

speed dict

Alg─▒lama s├╝recinin farkl─▒ b├Âl├╝mlerinin y├╝r├╝tme s├╝resini saklamak i├žin bir s├Âzl├╝k.

Y├Ântemler:

─░sim A├ž─▒klama
process

Metrik sonu├žlar─▒n─▒ en son tahmin grubuyla g├╝nceller.

keys

Hesaplanan alg─▒lama metriklerine eri┼čmek i├žin bir anahtar listesi d├Ând├╝r├╝r.

mean_results

Hesaplanan alg─▒lama metrikleri i├žin ortalama de─čerlerin bir listesini d├Ând├╝r├╝r.

class_result

Belirli bir s─▒n─▒f i├žin hesaplanan alg─▒lama metrikleri i├žin de─čerlerin bir listesini d├Ând├╝r├╝r.

maps

Farkl─▒ IoU e┼čikleri i├žin ortalama ortalama hassasiyet (mAP) de─čerleri s├Âzl├╝─č├╝ d├Ând├╝r├╝r.

fitness

Hesaplanan alg─▒lama metriklerine dayal─▒ olarak uygunluk puan─▒n─▒ hesaplar.

ap_class_index

Ortalama hassasiyet (AP) de─čerlerine g├Âre s─▒ralanm─▒┼č s─▒n─▒f indekslerinin bir listesini d├Ând├╝r├╝r.

results_dict

Alg─▒lama metrik anahtarlar─▒n─▒ hesaplanan de─čerleriyle e┼čleyen bir s├Âzl├╝k d├Ând├╝r├╝r.

curves

TODO

curves_results

TODO

Kaynak kodu ultralytics/utils/metrics.py
class DetMetrics(SimpleClass):
    """
    This class is a utility class for computing detection metrics such as precision, recall, and mean average precision
    (mAP) of an object detection model.

    Args:
        save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory.
        plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False.
        on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
        names (tuple of str): A tuple of strings that represents the names of the classes. Defaults to an empty tuple.

    Attributes:
        save_dir (Path): A path to the directory where the output plots will be saved.
        plot (bool): A flag that indicates whether to plot the precision-recall curves for each class.
        on_plot (func): An optional callback to pass plots path and data when they are rendered.
        names (tuple of str): A tuple of strings that represents the names of the classes.
        box (Metric): An instance of the Metric class for storing the results of the detection metrics.
        speed (dict): A dictionary for storing the execution time of different parts of the detection process.

    Methods:
        process(tp, conf, pred_cls, target_cls): Updates the metric results with the latest batch of predictions.
        keys: Returns a list of keys for accessing the computed detection metrics.
        mean_results: Returns a list of mean values for the computed detection metrics.
        class_result(i): Returns a list of values for the computed detection metrics for a specific class.
        maps: Returns a dictionary of mean average precision (mAP) values for different IoU thresholds.
        fitness: Computes the fitness score based on the computed detection metrics.
        ap_class_index: Returns a list of class indices sorted by their average precision (AP) values.
        results_dict: Returns a dictionary that maps detection metric keys to their computed values.
        curves: TODO
        curves_results: TODO
    """

    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
        """Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names."""
        self.save_dir = save_dir
        self.plot = plot
        self.on_plot = on_plot
        self.names = names
        self.box = Metric()
        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
        self.task = "detect"

    def process(self, tp, conf, pred_cls, target_cls):
        """Process predicted results for object detection and update metrics."""
        results = ap_per_class(
            tp,
            conf,
            pred_cls,
            target_cls,
            plot=self.plot,
            save_dir=self.save_dir,
            names=self.names,
            on_plot=self.on_plot,
        )[2:]
        self.box.nc = len(self.names)
        self.box.update(results)

    @property
    def keys(self):
        """Returns a list of keys for accessing specific metrics."""
        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]

    def mean_results(self):
        """Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
        return self.box.mean_results()

    def class_result(self, i):
        """Return the result of evaluating the performance of an object detection model on a specific class."""
        return self.box.class_result(i)

    @property
    def maps(self):
        """Returns mean Average Precision (mAP) scores per class."""
        return self.box.maps

    @property
    def fitness(self):
        """Returns the fitness of box object."""
        return self.box.fitness()

    @property
    def ap_class_index(self):
        """Returns the average precision index per class."""
        return self.box.ap_class_index

    @property
    def results_dict(self):
        """Returns dictionary of computed performance metrics and statistics."""
        return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))

    @property
    def curves(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return ["Precision-Recall(B)", "F1-Confidence(B)", "Precision-Confidence(B)", "Recall-Confidence(B)"]

    @property
    def curves_results(self):
        """Returns dictionary of computed performance metrics and statistics."""
        return self.box.curves_results

ap_class_index property

S─▒n─▒f ba┼č─▒na ortalama hassasiyet indeksini d├Ând├╝r├╝r.

curves property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

curves_results property

Hesaplanan performans ├Âl├ž├╝mlerinin ve istatistiklerinin s├Âzl├╝─č├╝n├╝ d├Ând├╝r├╝r.

fitness property

Kutu nesnesinin uygunlu─čunu d├Ând├╝r├╝r.

keys property

Belirli metriklere eri┼čmek i├žin bir anahtar listesi d├Ând├╝r├╝r.

maps property

S─▒n─▒f ba┼č─▒na ortalama Ortalama Hassasiyet (mAP) puanlar─▒n─▒ d├Ând├╝r├╝r.

results_dict property

Hesaplanan performans ├Âl├ž├╝mlerinin ve istatistiklerinin s├Âzl├╝─č├╝n├╝ d├Ând├╝r├╝r.

__init__(save_dir=Path('.'), plot=False, on_plot=None, names=())

Bir DetMetrics ├Ârne─čini kaydetme dizini, ├žizim bayra─č─▒, geri arama i┼člevi ve s─▒n─▒f adlar─▒yla ba┼člat─▒n.

Kaynak kodu ultralytics/utils/metrics.py
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
    """Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names."""
    self.save_dir = save_dir
    self.plot = plot
    self.on_plot = on_plot
    self.names = names
    self.box = Metric()
    self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
    self.task = "detect"

class_result(i)

Belirli bir s─▒n─▒f ├╝zerinde bir nesne alg─▒lama modelinin performans─▒n─▒ de─čerlendirmenin sonucunu d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def class_result(self, i):
    """Return the result of evaluating the performance of an object detection model on a specific class."""
    return self.box.class_result(i)

mean_results()

Alg─▒lanan nesnelerin ortalamas─▒n─▒ hesaplay─▒n ve hassasiyet, geri ├ža─č─▒rma, mAP50 ve mAP50-95 de─čerlerini d├Ând├╝r├╝n.

Kaynak kodu ultralytics/utils/metrics.py
def mean_results(self):
    """Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
    return self.box.mean_results()

process(tp, conf, pred_cls, target_cls)

Nesne alg─▒lama ve g├╝ncelleme metrikleri i├žin ├Âng├Âr├╝len sonu├žlar─▒ i┼čleyin.

Kaynak kodu ultralytics/utils/metrics.py
def process(self, tp, conf, pred_cls, target_cls):
    """Process predicted results for object detection and update metrics."""
    results = ap_per_class(
        tp,
        conf,
        pred_cls,
        target_cls,
        plot=self.plot,
        save_dir=self.save_dir,
        names=self.names,
        on_plot=self.on_plot,
    )[2:]
    self.box.nc = len(self.names)
    self.box.update(results)



ultralytics.utils.metrics.SegmentMetrics

├ťsler: SimpleClass

Belirli bir s─▒n─▒f k├╝mesi ├╝zerinde alg─▒lama ve segmentasyon metriklerini hesaplar ve toplar.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
save_dir Path

├ç─▒kt─▒ ├žizimlerinin kaydedilece─či dizinin yolu. Varsay─▒lan de─čer ge├žerli dizindir.

Path('.')
plot bool

Alg─▒lama ve segmentasyon ├žizimlerinin kaydedilip kaydedilmeyece─či. Varsay─▒lan de─čer False'dir.

False
on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama. Varsay─▒lan de─čer Yok'tur.

None
names list

S─▒n─▒f adlar─▒n─▒n listesi. Varsay─▒lan de─čer bo┼č bir listedir.

()

Nitelikler:

─░sim Tip A├ž─▒klama
save_dir Path

├ç─▒kt─▒ grafiklerinin kaydedilece─či dizinin yolu.

plot bool

Alg─▒lama ve segmentasyon ├žizimlerinin kaydedilip kaydedilmeyece─či.

on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama.

names list

S─▒n─▒f adlar─▒n─▒n listesi.

box Metric

Kutu alg─▒lama metriklerini hesaplamak i├žin Metrik s─▒n─▒f─▒n─▒n bir ├Ârne─či.

seg Metric

Maske segmentasyon metriklerini hesaplamak i├žin Metrik s─▒n─▒f─▒n─▒n bir ├Ârne─či.

speed dict

├ç─▒kar─▒m─▒n farkl─▒ a┼čamalar─▒nda ge├žen s├╝reyi saklamak i├žin s├Âzl├╝k.

Y├Ântemler:

─░sim A├ž─▒klama
process

Verilen tahminler k├╝mesi ├╝zerinde metrikleri i┼čler.

mean_results

T├╝m s─▒n─▒flar ├╝zerinde alg─▒lama ve segmentasyon metriklerinin ortalamas─▒n─▒ d├Ând├╝r├╝r.

class_result

S─▒n─▒f─▒n alg─▒lama ve segmentasyon metriklerini d├Ând├╝r├╝r i.

maps

IoU e┼čikleri i├žin 0,50 ile 0,95 aras─▒nda de─či┼čen ortalama Ortalama Hassasiyet (mAP) puanlar─▒n─▒ d├Ând├╝r├╝r.

fitness

Metriklerin tek bir a─č─▒rl─▒kl─▒ kombinasyonu olan uygunluk puanlar─▒n─▒ d├Ând├╝r├╝r.

ap_class_index

Ortalama Hassasiyeti (AP) hesaplamak i├žin kullan─▒lan s─▒n─▒flar─▒n indekslerinin listesini d├Ând├╝r├╝r.

results_dict

T├╝m alg─▒lama ve segmentasyon metriklerini ve uygunluk puan─▒n─▒ i├žeren s├Âzl├╝─č├╝ d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
class SegmentMetrics(SimpleClass):
    """
    Calculates and aggregates detection and segmentation metrics over a given set of classes.

    Args:
        save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
        plot (bool): Whether to save the detection and segmentation plots. Default is False.
        on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
        names (list): List of class names. Default is an empty list.

    Attributes:
        save_dir (Path): Path to the directory where the output plots should be saved.
        plot (bool): Whether to save the detection and segmentation plots.
        on_plot (func): An optional callback to pass plots path and data when they are rendered.
        names (list): List of class names.
        box (Metric): An instance of the Metric class to calculate box detection metrics.
        seg (Metric): An instance of the Metric class to calculate mask segmentation metrics.
        speed (dict): Dictionary to store the time taken in different phases of inference.

    Methods:
        process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions.
        mean_results(): Returns the mean of the detection and segmentation metrics over all the classes.
        class_result(i): Returns the detection and segmentation metrics of class `i`.
        maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95.
        fitness: Returns the fitness scores, which are a single weighted combination of metrics.
        ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP).
        results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
    """

    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
        """Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names."""
        self.save_dir = save_dir
        self.plot = plot
        self.on_plot = on_plot
        self.names = names
        self.box = Metric()
        self.seg = Metric()
        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
        self.task = "segment"

    def process(self, tp, tp_m, conf, pred_cls, target_cls):
        """
        Processes the detection and segmentation metrics over the given set of predictions.

        Args:
            tp (list): List of True Positive boxes.
            tp_m (list): List of True Positive masks.
            conf (list): List of confidence scores.
            pred_cls (list): List of predicted classes.
            target_cls (list): List of target classes.
        """

        results_mask = ap_per_class(
            tp_m,
            conf,
            pred_cls,
            target_cls,
            plot=self.plot,
            on_plot=self.on_plot,
            save_dir=self.save_dir,
            names=self.names,
            prefix="Mask",
        )[2:]
        self.seg.nc = len(self.names)
        self.seg.update(results_mask)
        results_box = ap_per_class(
            tp,
            conf,
            pred_cls,
            target_cls,
            plot=self.plot,
            on_plot=self.on_plot,
            save_dir=self.save_dir,
            names=self.names,
            prefix="Box",
        )[2:]
        self.box.nc = len(self.names)
        self.box.update(results_box)

    @property
    def keys(self):
        """Returns a list of keys for accessing metrics."""
        return [
            "metrics/precision(B)",
            "metrics/recall(B)",
            "metrics/mAP50(B)",
            "metrics/mAP50-95(B)",
            "metrics/precision(M)",
            "metrics/recall(M)",
            "metrics/mAP50(M)",
            "metrics/mAP50-95(M)",
        ]

    def mean_results(self):
        """Return the mean metrics for bounding box and segmentation results."""
        return self.box.mean_results() + self.seg.mean_results()

    def class_result(self, i):
        """Returns classification results for a specified class index."""
        return self.box.class_result(i) + self.seg.class_result(i)

    @property
    def maps(self):
        """Returns mAP scores for object detection and semantic segmentation models."""
        return self.box.maps + self.seg.maps

    @property
    def fitness(self):
        """Get the fitness score for both segmentation and bounding box models."""
        return self.seg.fitness() + self.box.fitness()

    @property
    def ap_class_index(self):
        """Boxes and masks have the same ap_class_index."""
        return self.box.ap_class_index

    @property
    def results_dict(self):
        """Returns results of object detection model for evaluation."""
        return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))

    @property
    def curves(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return [
            "Precision-Recall(B)",
            "F1-Confidence(B)",
            "Precision-Confidence(B)",
            "Recall-Confidence(B)",
            "Precision-Recall(M)",
            "F1-Confidence(M)",
            "Precision-Confidence(M)",
            "Recall-Confidence(M)",
        ]

    @property
    def curves_results(self):
        """Returns dictionary of computed performance metrics and statistics."""
        return self.box.curves_results + self.seg.curves_results

ap_class_index property

Kutular ve maskeler ayn─▒ ap_class_index'e sahiptir.

curves property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

curves_results property

Hesaplanan performans ├Âl├ž├╝mlerinin ve istatistiklerinin s├Âzl├╝─č├╝n├╝ d├Ând├╝r├╝r.

fitness property

Hem segmentasyon hem de s─▒n─▒rlay─▒c─▒ kutu modelleri i├žin uygunluk puan─▒n─▒ al─▒n.

keys property

Metriklere eri┼čmek i├žin bir anahtar listesi d├Ând├╝r├╝r.

maps property

Nesne alg─▒lama ve anlamsal segmentasyon modelleri i├žin mAP puanlar─▒n─▒ d├Ând├╝r├╝r.

results_dict property

De─čerlendirme i├žin nesne alg─▒lama modelinin sonu├žlar─▒n─▒ d├Ând├╝r├╝r.

__init__(save_dir=Path('.'), plot=False, on_plot=None, names=())

Bir SegmentMetrics ├Ârne─čini kaydetme dizini, ├žizim bayra─č─▒, geri arama i┼člevi ve s─▒n─▒f adlar─▒yla ba┼člat─▒n.

Kaynak kodu ultralytics/utils/metrics.py
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
    """Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names."""
    self.save_dir = save_dir
    self.plot = plot
    self.on_plot = on_plot
    self.names = names
    self.box = Metric()
    self.seg = Metric()
    self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
    self.task = "segment"

class_result(i)

Belirtilen bir s─▒n─▒f dizini i├žin s─▒n─▒fland─▒rma sonu├žlar─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def class_result(self, i):
    """Returns classification results for a specified class index."""
    return self.box.class_result(i) + self.seg.class_result(i)

mean_results()

S─▒n─▒rlay─▒c─▒ kutu ve segmentasyon sonu├žlar─▒ i├žin ortalama metrikleri d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def mean_results(self):
    """Return the mean metrics for bounding box and segmentation results."""
    return self.box.mean_results() + self.seg.mean_results()

process(tp, tp_m, conf, pred_cls, target_cls)

Verilen tahminler k├╝mesi ├╝zerinde alg─▒lama ve segmentasyon metriklerini i┼čler.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
tp list

Ger├žek Pozitif kutular─▒n listesi.

gerekli
tp_m list

Ger├žek Pozitif maskelerin listesi.

gerekli
conf list

G├╝ven puanlar─▒n─▒n listesi.

gerekli
pred_cls list

Tahmin edilen s─▒n─▒flar─▒n listesi.

gerekli
target_cls list

Hedef s─▒n─▒flar─▒n listesi.

gerekli
Kaynak kodu ultralytics/utils/metrics.py
def process(self, tp, tp_m, conf, pred_cls, target_cls):
    """
    Processes the detection and segmentation metrics over the given set of predictions.

    Args:
        tp (list): List of True Positive boxes.
        tp_m (list): List of True Positive masks.
        conf (list): List of confidence scores.
        pred_cls (list): List of predicted classes.
        target_cls (list): List of target classes.
    """

    results_mask = ap_per_class(
        tp_m,
        conf,
        pred_cls,
        target_cls,
        plot=self.plot,
        on_plot=self.on_plot,
        save_dir=self.save_dir,
        names=self.names,
        prefix="Mask",
    )[2:]
    self.seg.nc = len(self.names)
    self.seg.update(results_mask)
    results_box = ap_per_class(
        tp,
        conf,
        pred_cls,
        target_cls,
        plot=self.plot,
        on_plot=self.on_plot,
        save_dir=self.save_dir,
        names=self.names,
        prefix="Box",
    )[2:]
    self.box.nc = len(self.names)
    self.box.update(results_box)



ultralytics.utils.metrics.PoseMetrics

├ťsler: SegmentMetrics

Belirli bir s─▒n─▒f k├╝mesi ├╝zerinde alg─▒lama ve poz metriklerini hesaplar ve toplar.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
save_dir Path

├ç─▒kt─▒ ├žizimlerinin kaydedilece─či dizinin yolu. Varsay─▒lan de─čer ge├žerli dizindir.

Path('.')
plot bool

Alg─▒lama ve segmentasyon ├žizimlerinin kaydedilip kaydedilmeyece─či. Varsay─▒lan de─čer False'dir.

False
on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama. Varsay─▒lan de─čer Yok'tur.

None
names list

S─▒n─▒f adlar─▒n─▒n listesi. Varsay─▒lan de─čer bo┼č bir listedir.

()

Nitelikler:

─░sim Tip A├ž─▒klama
save_dir Path

├ç─▒kt─▒ grafiklerinin kaydedilece─či dizinin yolu.

plot bool

Alg─▒lama ve segmentasyon ├žizimlerinin kaydedilip kaydedilmeyece─či.

on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin iste─če ba─čl─▒ bir geri arama.

names list

S─▒n─▒f adlar─▒n─▒n listesi.

box Metric

Kutu alg─▒lama metriklerini hesaplamak i├žin Metrik s─▒n─▒f─▒n─▒n bir ├Ârne─či.

pose Metric

Maske segmentasyon metriklerini hesaplamak i├žin Metrik s─▒n─▒f─▒n─▒n bir ├Ârne─či.

speed dict

├ç─▒kar─▒m─▒n farkl─▒ a┼čamalar─▒nda ge├žen s├╝reyi saklamak i├žin s├Âzl├╝k.

Y├Ântemler:

─░sim A├ž─▒klama
process

Verilen tahminler k├╝mesi ├╝zerinde metrikleri i┼čler.

mean_results

T├╝m s─▒n─▒flar ├╝zerinde alg─▒lama ve segmentasyon metriklerinin ortalamas─▒n─▒ d├Ând├╝r├╝r.

class_result

S─▒n─▒f─▒n alg─▒lama ve segmentasyon metriklerini d├Ând├╝r├╝r i.

maps

IoU e┼čikleri i├žin 0,50 ile 0,95 aras─▒nda de─či┼čen ortalama Ortalama Hassasiyet (mAP) puanlar─▒n─▒ d├Ând├╝r├╝r.

fitness

Metriklerin tek bir a─č─▒rl─▒kl─▒ kombinasyonu olan uygunluk puanlar─▒n─▒ d├Ând├╝r├╝r.

ap_class_index

Ortalama Hassasiyeti (AP) hesaplamak i├žin kullan─▒lan s─▒n─▒flar─▒n indekslerinin listesini d├Ând├╝r├╝r.

results_dict

T├╝m alg─▒lama ve segmentasyon metriklerini ve uygunluk puan─▒n─▒ i├žeren s├Âzl├╝─č├╝ d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
class PoseMetrics(SegmentMetrics):
    """
    Calculates and aggregates detection and pose metrics over a given set of classes.

    Args:
        save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory.
        plot (bool): Whether to save the detection and segmentation plots. Default is False.
        on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None.
        names (list): List of class names. Default is an empty list.

    Attributes:
        save_dir (Path): Path to the directory where the output plots should be saved.
        plot (bool): Whether to save the detection and segmentation plots.
        on_plot (func): An optional callback to pass plots path and data when they are rendered.
        names (list): List of class names.
        box (Metric): An instance of the Metric class to calculate box detection metrics.
        pose (Metric): An instance of the Metric class to calculate mask segmentation metrics.
        speed (dict): Dictionary to store the time taken in different phases of inference.

    Methods:
        process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions.
        mean_results(): Returns the mean of the detection and segmentation metrics over all the classes.
        class_result(i): Returns the detection and segmentation metrics of class `i`.
        maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95.
        fitness: Returns the fitness scores, which are a single weighted combination of metrics.
        ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP).
        results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score.
    """

    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
        """Initialize the PoseMetrics class with directory path, class names, and plotting options."""
        super().__init__(save_dir, plot, names)
        self.save_dir = save_dir
        self.plot = plot
        self.on_plot = on_plot
        self.names = names
        self.box = Metric()
        self.pose = Metric()
        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
        self.task = "pose"

    def process(self, tp, tp_p, conf, pred_cls, target_cls):
        """
        Processes the detection and pose metrics over the given set of predictions.

        Args:
            tp (list): List of True Positive boxes.
            tp_p (list): List of True Positive keypoints.
            conf (list): List of confidence scores.
            pred_cls (list): List of predicted classes.
            target_cls (list): List of target classes.
        """

        results_pose = ap_per_class(
            tp_p,
            conf,
            pred_cls,
            target_cls,
            plot=self.plot,
            on_plot=self.on_plot,
            save_dir=self.save_dir,
            names=self.names,
            prefix="Pose",
        )[2:]
        self.pose.nc = len(self.names)
        self.pose.update(results_pose)
        results_box = ap_per_class(
            tp,
            conf,
            pred_cls,
            target_cls,
            plot=self.plot,
            on_plot=self.on_plot,
            save_dir=self.save_dir,
            names=self.names,
            prefix="Box",
        )[2:]
        self.box.nc = len(self.names)
        self.box.update(results_box)

    @property
    def keys(self):
        """Returns list of evaluation metric keys."""
        return [
            "metrics/precision(B)",
            "metrics/recall(B)",
            "metrics/mAP50(B)",
            "metrics/mAP50-95(B)",
            "metrics/precision(P)",
            "metrics/recall(P)",
            "metrics/mAP50(P)",
            "metrics/mAP50-95(P)",
        ]

    def mean_results(self):
        """Return the mean results of box and pose."""
        return self.box.mean_results() + self.pose.mean_results()

    def class_result(self, i):
        """Return the class-wise detection results for a specific class i."""
        return self.box.class_result(i) + self.pose.class_result(i)

    @property
    def maps(self):
        """Returns the mean average precision (mAP) per class for both box and pose detections."""
        return self.box.maps + self.pose.maps

    @property
    def fitness(self):
        """Computes classification metrics and speed using the `targets` and `pred` inputs."""
        return self.pose.fitness() + self.box.fitness()

    @property
    def curves(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return [
            "Precision-Recall(B)",
            "F1-Confidence(B)",
            "Precision-Confidence(B)",
            "Recall-Confidence(B)",
            "Precision-Recall(P)",
            "F1-Confidence(P)",
            "Precision-Confidence(P)",
            "Recall-Confidence(P)",
        ]

    @property
    def curves_results(self):
        """Returns dictionary of computed performance metrics and statistics."""
        return self.box.curves_results + self.pose.curves_results

curves property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

curves_results property

Hesaplanan performans ├Âl├ž├╝mlerinin ve istatistiklerinin s├Âzl├╝─č├╝n├╝ d├Ând├╝r├╝r.

fitness property

kullanarak s─▒n─▒fland─▒rma metriklerini ve h─▒z─▒n─▒ hesaplar. targets ve pred Girdiler.

keys property

De─čerlendirme metrik anahtarlar─▒n─▒n listesini d├Ând├╝r├╝r.

maps property

Hem kutu hem de poz alg─▒lamalar─▒ i├žin s─▒n─▒f ba┼č─▒na ortalama ortalama hassasiyeti (mAP) verir.

__init__(save_dir=Path('.'), plot=False, on_plot=None, names=())

PoseMetrics s─▒n─▒f─▒n─▒ dizin yolu, s─▒n─▒f adlar─▒ ve ├žizim se├ženekleriyle ba┼člat─▒n.

Kaynak kodu ultralytics/utils/metrics.py
def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
    """Initialize the PoseMetrics class with directory path, class names, and plotting options."""
    super().__init__(save_dir, plot, names)
    self.save_dir = save_dir
    self.plot = plot
    self.on_plot = on_plot
    self.names = names
    self.box = Metric()
    self.pose = Metric()
    self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
    self.task = "pose"

class_result(i)

Belirli bir i s─▒n─▒f─▒ i├žin s─▒n─▒f baz─▒nda alg─▒lama sonu├žlar─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def class_result(self, i):
    """Return the class-wise detection results for a specific class i."""
    return self.box.class_result(i) + self.pose.class_result(i)

mean_results()

Kutu ve pozun ortalama sonu├žlar─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def mean_results(self):
    """Return the mean results of box and pose."""
    return self.box.mean_results() + self.pose.mean_results()

process(tp, tp_p, conf, pred_cls, target_cls)

Verilen tahminler k├╝mesi ├╝zerinde alg─▒lama ve poz metriklerini i┼čler.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
tp list

Ger├žek Pozitif kutular─▒n listesi.

gerekli
tp_p list

Ger├žek Pozitif kilit noktalar─▒n─▒n listesi.

gerekli
conf list

G├╝ven puanlar─▒n─▒n listesi.

gerekli
pred_cls list

Tahmin edilen s─▒n─▒flar─▒n listesi.

gerekli
target_cls list

Hedef s─▒n─▒flar─▒n listesi.

gerekli
Kaynak kodu ultralytics/utils/metrics.py
def process(self, tp, tp_p, conf, pred_cls, target_cls):
    """
    Processes the detection and pose metrics over the given set of predictions.

    Args:
        tp (list): List of True Positive boxes.
        tp_p (list): List of True Positive keypoints.
        conf (list): List of confidence scores.
        pred_cls (list): List of predicted classes.
        target_cls (list): List of target classes.
    """

    results_pose = ap_per_class(
        tp_p,
        conf,
        pred_cls,
        target_cls,
        plot=self.plot,
        on_plot=self.on_plot,
        save_dir=self.save_dir,
        names=self.names,
        prefix="Pose",
    )[2:]
    self.pose.nc = len(self.names)
    self.pose.update(results_pose)
    results_box = ap_per_class(
        tp,
        conf,
        pred_cls,
        target_cls,
        plot=self.plot,
        on_plot=self.on_plot,
        save_dir=self.save_dir,
        names=self.names,
        prefix="Box",
    )[2:]
    self.box.nc = len(self.names)
    self.box.update(results_box)



ultralytics.utils.metrics.ClassifyMetrics

├ťsler: SimpleClass

En iyi 1 ve en iyi 5 do─čruluk dahil olmak ├╝zere s─▒n─▒fland─▒rma metriklerini hesaplamak i├žin s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
top1 float

─░lk 1 do─čruluk.

top5 float

─░lk 5 do─čruluk.

speed Dict[str, float]

Boru hatt─▒ndaki her ad─▒m i├žin ge├žen s├╝reyi i├žeren bir s├Âzl├╝k.

├ľzellikler

uygunluk (float): ─░lk 5 do─črulu─ča e┼čit olan modelin uygunlu─ču. results_dict (Dict[str, Union[float, str]]): S─▒n─▒fland─▒rma metriklerini ve uygunlu─ču i├žeren bir s├Âzl├╝k. keys (Liste[str]): results_dict i├žin bir anahtar listesi.

Y├Ântemler:

─░sim A├ž─▒klama
process

S─▒n─▒fland─▒rma metriklerini hesaplamak i├žin hedefleri ve tahminleri i┼čler.

Kaynak kodu ultralytics/utils/metrics.py
class ClassifyMetrics(SimpleClass):
    """
    Class for computing classification metrics including top-1 and top-5 accuracy.

    Attributes:
        top1 (float): The top-1 accuracy.
        top5 (float): The top-5 accuracy.
        speed (Dict[str, float]): A dictionary containing the time taken for each step in the pipeline.

    Properties:
        fitness (float): The fitness of the model, which is equal to top-5 accuracy.
        results_dict (Dict[str, Union[float, str]]): A dictionary containing the classification metrics and fitness.
        keys (List[str]): A list of keys for the results_dict.

    Methods:
        process(targets, pred): Processes the targets and predictions to compute classification metrics.
    """

    def __init__(self) -> None:
        """Initialize a ClassifyMetrics instance."""
        self.top1 = 0
        self.top5 = 0
        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
        self.task = "classify"

    def process(self, targets, pred):
        """Target classes and predicted classes."""
        pred, targets = torch.cat(pred), torch.cat(targets)
        correct = (targets[:, None] == pred).float()
        acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy
        self.top1, self.top5 = acc.mean(0).tolist()

    @property
    def fitness(self):
        """Returns mean of top-1 and top-5 accuracies as fitness score."""
        return (self.top1 + self.top5) / 2

    @property
    def results_dict(self):
        """Returns a dictionary with model's performance metrics and fitness score."""
        return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness]))

    @property
    def keys(self):
        """Returns a list of keys for the results_dict property."""
        return ["metrics/accuracy_top1", "metrics/accuracy_top5"]

    @property
    def curves(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return []

    @property
    def curves_results(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return []

curves property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

curves_results property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

fitness property

─░lk 1 ve ilk 5 do─čruluklar─▒n ortalamas─▒n─▒ uygunluk puan─▒ olarak d├Ând├╝r├╝r.

keys property

results_dict ├Âzelli─či i├žin bir anahtar listesi d├Ând├╝r├╝r.

results_dict property

Modelin performans ├Âl├ž├╝mlerini ve uygunluk puan─▒n─▒ i├žeren bir s├Âzl├╝k d├Ând├╝r├╝r.

__init__()

Bir ClassifyMetrics ├Ârne─čini ba┼člat─▒n.

Kaynak kodu ultralytics/utils/metrics.py
def __init__(self) -> None:
    """Initialize a ClassifyMetrics instance."""
    self.top1 = 0
    self.top5 = 0
    self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
    self.task = "classify"

process(targets, pred)

Hedef s─▒n─▒flar ve ├Âng├Âr├╝len s─▒n─▒flar.

Kaynak kodu ultralytics/utils/metrics.py
def process(self, targets, pred):
    """Target classes and predicted classes."""
    pred, targets = torch.cat(pred), torch.cat(targets)
    correct = (targets[:, None] == pred).float()
    acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy
    self.top1, self.top5 = acc.mean(0).tolist()



ultralytics.utils.metrics.OBBMetrics

├ťsler: SimpleClass

Kaynak kodu ultralytics/utils/metrics.py
class OBBMetrics(SimpleClass):
    def __init__(self, save_dir=Path("."), plot=False, on_plot=None, names=()) -> None:
        self.save_dir = save_dir
        self.plot = plot
        self.on_plot = on_plot
        self.names = names
        self.box = Metric()
        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}

    def process(self, tp, conf, pred_cls, target_cls):
        """Process predicted results for object detection and update metrics."""
        results = ap_per_class(
            tp,
            conf,
            pred_cls,
            target_cls,
            plot=self.plot,
            save_dir=self.save_dir,
            names=self.names,
            on_plot=self.on_plot,
        )[2:]
        self.box.nc = len(self.names)
        self.box.update(results)

    @property
    def keys(self):
        """Returns a list of keys for accessing specific metrics."""
        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]

    def mean_results(self):
        """Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
        return self.box.mean_results()

    def class_result(self, i):
        """Return the result of evaluating the performance of an object detection model on a specific class."""
        return self.box.class_result(i)

    @property
    def maps(self):
        """Returns mean Average Precision (mAP) scores per class."""
        return self.box.maps

    @property
    def fitness(self):
        """Returns the fitness of box object."""
        return self.box.fitness()

    @property
    def ap_class_index(self):
        """Returns the average precision index per class."""
        return self.box.ap_class_index

    @property
    def results_dict(self):
        """Returns dictionary of computed performance metrics and statistics."""
        return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))

    @property
    def curves(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return []

    @property
    def curves_results(self):
        """Returns a list of curves for accessing specific metrics curves."""
        return []

ap_class_index property

S─▒n─▒f ba┼č─▒na ortalama hassasiyet indeksini d├Ând├╝r├╝r.

curves property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

curves_results property

Belirli metrik e─črilerine eri┼čmek i├žin bir e─čri listesi d├Ând├╝r├╝r.

fitness property

Kutu nesnesinin uygunlu─čunu d├Ând├╝r├╝r.

keys property

Belirli metriklere eri┼čmek i├žin bir anahtar listesi d├Ând├╝r├╝r.

maps property

S─▒n─▒f ba┼č─▒na ortalama Ortalama Hassasiyet (mAP) puanlar─▒n─▒ d├Ând├╝r├╝r.

results_dict property

Hesaplanan performans ├Âl├ž├╝mlerinin ve istatistiklerinin s├Âzl├╝─č├╝n├╝ d├Ând├╝r├╝r.

class_result(i)

Belirli bir s─▒n─▒f ├╝zerinde bir nesne alg─▒lama modelinin performans─▒n─▒ de─čerlendirmenin sonucunu d├Ând├╝r├╝r.

Kaynak kodu ultralytics/utils/metrics.py
def class_result(self, i):
    """Return the result of evaluating the performance of an object detection model on a specific class."""
    return self.box.class_result(i)

mean_results()

Alg─▒lanan nesnelerin ortalamas─▒n─▒ hesaplay─▒n ve hassasiyet, geri ├ža─č─▒rma, mAP50 ve mAP50-95 de─čerlerini d├Ând├╝r├╝n.

Kaynak kodu ultralytics/utils/metrics.py
def mean_results(self):
    """Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95."""
    return self.box.mean_results()

process(tp, conf, pred_cls, target_cls)

Nesne alg─▒lama ve g├╝ncelleme metrikleri i├žin ├Âng├Âr├╝len sonu├žlar─▒ i┼čleyin.

Kaynak kodu ultralytics/utils/metrics.py
def process(self, tp, conf, pred_cls, target_cls):
    """Process predicted results for object detection and update metrics."""
    results = ap_per_class(
        tp,
        conf,
        pred_cls,
        target_cls,
        plot=self.plot,
        save_dir=self.save_dir,
        names=self.names,
        on_plot=self.on_plot,
    )[2:]
    self.box.nc = len(self.names)
    self.box.update(results)



ultralytics.utils.metrics.bbox_ioa(box1, box2, iou=False, eps=1e-07)

Kutu1 ve kutu2 verilen kutu2 alan─▒ ├╝zerindeki kesi┼čimi hesaplay─▒n. Kutular x1y1x2y2 bi├žimindedir.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
box1 ndarray

n s─▒n─▒rlay─▒c─▒ kutuyu temsil eden (n, 4) ┼čeklinde bir numpy dizisi.

gerekli
box2 ndarray

m s─▒n─▒rlay─▒c─▒ kutuyu temsil eden (m, 4) ┼čeklinde bir numpy dizisi.

gerekli
iou bool

True ise standart IoU'yu hesaplay─▒n, aksi takdirde inter_area/box2_area de─čerini d├Ând├╝r├╝n.

False
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
ndarray

Kutu2 alan─▒ ├╝zerindeki kesi┼čimi temsil eden (n, m) ┼čeklinde bir numpy dizisi.

Kaynak kodu ultralytics/utils/metrics.py
def bbox_ioa(box1, box2, iou=False, eps=1e-7):
    """
    Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format.

    Args:
        box1 (np.ndarray): A numpy array of shape (n, 4) representing n bounding boxes.
        box2 (np.ndarray): A numpy array of shape (m, 4) representing m bounding boxes.
        iou (bool): Calculate the standard IoU if True else return inter_area/box2_area.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (np.ndarray): A numpy array of shape (n, m) representing the intersection over box2 area.
    """

    # Get the coordinates of bounding boxes
    b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
    b2_x1, b2_y1, b2_x2, b2_y2 = box2.T

    # Intersection area
    inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * (
        np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)
    ).clip(0)

    # Box2 area
    area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
    if iou:
        box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
        area = area + box1_area[:, None] - inter_area

    # Intersection over box2 area
    return inter_area / (area + eps)



ultralytics.utils.metrics.box_iou(box1, box2, eps=1e-07)

Kutular─▒n kesi┼čim-over-union (IoU) hesaplay─▒n. Her iki kutu k├╝mesinin de (x1, y1, x2, y2) bi├žiminde olmas─▒ beklenir. https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py temel al─▒nm─▒┼čt─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
box1 Tensor

N s─▒n─▒rlay─▒c─▒ kutuyu temsil eden (N, 4) ┼čeklinde bir tensor .

gerekli
box2 Tensor

M s─▒n─▒rlay─▒c─▒ kutuyu temsil eden (M, 4) ┼čeklinde bir tensor .

gerekli
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
Tensor

Bir NxM tensor kutu1 ve kutu2'deki her ├Â─če i├žin ├žift y├Ânl├╝ IoU de─čerlerini i├žerir.

Kaynak kodu ultralytics/utils/metrics.py
def box_iou(box1, box2, eps=1e-7):
    """
    Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
    Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py

    Args:
        box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes.
        box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.
    """

    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)

    # IoU = inter / (area1 + area2 - inter)
    return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)



ultralytics.utils.metrics.bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-07)

Kutu1(1, 4) ile kutu2(n, 4)'├╝n Birlik ├╝zerinden Kesi┼čimini (IoU) hesaplay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
box1 Tensor

┼×ekil (1, 4) ile tek bir s─▒n─▒rlay─▒c─▒ kutuyu temsil eden bir tensor .

gerekli
box2 Tensor

┼×ekil (n, 4) ile n s─▒n─▒rlay─▒c─▒ kutuyu temsil eden bir tensor .

gerekli
xywh bool

True ise, giri┼č kutular─▒ (x, y, w, h) bi├žimindedir. False ise, giri┼č kutular─▒ (x1, y1, x2, y2) bi├žimi. Varsay─▒lan de─čer True'dur.

True
GIoU bool

True ise, Genelle┼čtirilmi┼č IoU'yu hesaplar. Varsay─▒lan de─čer False'dir.

False
DIoU bool

True ise, Mesafe IoU'yu hesaplar. Varsay─▒lan de─čer False'dir.

False
CIoU bool

True ise, Tam IoU'yu hesaplar. Varsay─▒lan de─čer False'dir.

False
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
Tensor

Belirtilen bayraklara ba─čl─▒ olarak IoU, GIoU, DIoU veya CIoU de─čerleri.

Kaynak kodu ultralytics/utils/metrics.py
def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
    """
    Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4).

    Args:
        box1 (torch.Tensor): A tensor representing a single bounding box with shape (1, 4).
        box2 (torch.Tensor): A tensor representing n bounding boxes with shape (n, 4).
        xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in
                               (x1, y1, x2, y2) format. Defaults to True.
        GIoU (bool, optional): If True, calculate Generalized IoU. Defaults to False.
        DIoU (bool, optional): If True, calculate Distance IoU. Defaults to False.
        CIoU (bool, optional): If True, calculate Complete IoU. Defaults to False.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags.
    """

    # Get the coordinates of bounding boxes
    if xywh:  # transform from xywh to xyxy
        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
    else:  # x1, y1, x2, y2 = box1
        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
        w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps

    # Intersection area
    inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * (
        b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)
    ).clamp_(0)

    # Union Area
    union = w1 * h1 + w2 * h2 - inter + eps

    # IoU
    iou = inter / union
    if CIoU or DIoU or GIoU:
        cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1)  # convex (smallest enclosing box) width
        ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1)  # convex height
        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            c2 = cw.pow(2) + ch.pow(2) + eps  # convex diagonal squared
            rho2 = (
                (b2_x1 + b2_x2 - b1_x1 - b1_x2).pow(2) + (b2_y1 + b2_y2 - b1_y1 - b1_y2).pow(2)
            ) / 4  # center dist**2
            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
                v = (4 / math.pi**2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2)
                with torch.no_grad():
                    alpha = v / (v - iou + (1 + eps))
                return iou - (rho2 / c2 + v * alpha)  # CIoU
            return iou - rho2 / c2  # DIoU
        c_area = cw * ch + eps  # convex area
        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
    return iou  # IoU



ultralytics.utils.metrics.mask_iou(mask1, mask2, eps=1e-07)

Maske IoU'yu hesaplay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
mask1 Tensor

Bir tensor ┼čekli (N, n); burada N, temel ger├žek nesnelerinin say─▒s─▒ ve n ise g├Âr├╝nt├╝ geni┼čli─či ve y├╝ksekli─činin ├žarp─▒m─▒.

gerekli
mask2 Tensor

A tensor (M, n) ┼čeklinde olup burada M tahmin edilen nesnelerin say─▒s─▒ ve n ise g├Âr├╝nt├╝ geni┼čli─či ve y├╝ksekli─činin ├žarp─▒m─▒.

gerekli
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
Tensor

IoU maskelerini temsil eden (N, M) ┼čeklindeki bir tensor .

Kaynak kodu ultralytics/utils/metrics.py
def mask_iou(mask1, mask2, eps=1e-7):
    """
    Calculate masks IoU.

    Args:
        mask1 (torch.Tensor): A tensor of shape (N, n) where N is the number of ground truth objects and n is the
                        product of image width and height.
        mask2 (torch.Tensor): A tensor of shape (M, n) where M is the number of predicted objects and n is the
                        product of image width and height.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): A tensor of shape (N, M) representing masks IoU.
    """
    intersection = torch.matmul(mask1, mask2.T).clamp_(0)
    union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection  # (area1 + area2) - intersection
    return intersection / (union + eps)



ultralytics.utils.metrics.kpt_iou(kpt1, kpt2, area, sigma, eps=1e-07)

Nesne Anahtar Noktas─▒ Benzerli─čini (OKS) hesaplay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
kpt1 Tensor

Zemin ger├že─či anahtar noktalar─▒n─▒ temsil eden bir tensor ┼čekli (N, 17, 3).

gerekli
kpt2 Tensor

Tahmin edilen kilit noktalar─▒ temsil eden bir tensor ┼čekli (M, 17, 3).

gerekli
area Tensor

Zemin ger├že─činden alanlar─▒ temsil eden bir tensor ┼čekli (N,).

gerekli
sigma list

Anahtar noktas─▒ ├Âl├žeklerini temsil eden 17 de─čer i├žeren bir liste.

gerekli
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
Tensor

Anahtar nokta benzerliklerini temsil eden (N, M) ┼čeklindeki bir tensor .

Kaynak kodu ultralytics/utils/metrics.py
def kpt_iou(kpt1, kpt2, area, sigma, eps=1e-7):
    """
    Calculate Object Keypoint Similarity (OKS).

    Args:
        kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints.
        kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints.
        area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth.
        sigma (list): A list containing 17 values representing keypoint scales.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): A tensor of shape (N, M) representing keypoint similarities.
    """
    d = (kpt1[:, None, :, 0] - kpt2[..., 0]).pow(2) + (kpt1[:, None, :, 1] - kpt2[..., 1]).pow(2)  # (N, M, 17)
    sigma = torch.tensor(sigma, device=kpt1.device, dtype=kpt1.dtype)  # (17, )
    kpt_mask = kpt1[..., 2] != 0  # (N, 17)
    e = d / ((2 * sigma).pow(2) * (area[:, None, None] + eps) * 2)  # from cocoeval
    # e = d / ((area[None, :, None] + eps) * sigma) ** 2 / 2  # from formula
    return ((-e).exp() * kpt_mask[:, None]).sum(-1) / (kpt_mask.sum(-1)[:, None] + eps)



ultralytics.utils.metrics._get_covariance_matrix(boxes)

Obbs'den kovaryans matrisi olu┼čturma.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
boxes Tensor

xywhr format─▒nda, d├Ând├╝r├╝lm├╝┼č s─▒n─▒rlay─▒c─▒ kutular─▒ temsil eden (N, 5) ┼čeklinde bir tensor .

gerekli

─░ade:

Tip A├ž─▒klama
Tensor

Orijinal d├Ând├╝r├╝lm├╝┼č s─▒n─▒rlay─▒c─▒ kutulara kar┼č─▒l─▒k gelen kovaryans metriksleri.

Kaynak kodu ultralytics/utils/metrics.py
def _get_covariance_matrix(boxes):
    """
    Generating covariance matrix from obbs.

    Args:
        boxes (torch.Tensor): A tensor of shape (N, 5) representing rotated bounding boxes, with xywhr format.

    Returns:
        (torch.Tensor): Covariance metrixs corresponding to original rotated bounding boxes.
    """
    # Gaussian bounding boxes, ignore the center points (the first two columns) because they are not needed here.
    gbbs = torch.cat((boxes[:, 2:4].pow(2) / 12, boxes[:, 4:]), dim=-1)
    a, b, c = gbbs.split(1, dim=-1)
    cos = c.cos()
    sin = c.sin()
    cos2 = cos.pow(2)
    sin2 = sin.pow(2)
    return a * cos2 + b * sin2, a * sin2 + b * cos2, (a - b) * cos * sin



ultralytics.utils.metrics.probiou(obb1, obb2, CIoU=False, eps=1e-07)

Y├Ânlendirilmi┼č s─▒n─▒rlay─▒c─▒ kutular aras─▒ndaki prob IoU'yu hesaplay─▒n, https://arxiv.org/pdf/2106.06072v1.pdf.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
obb1 Tensor

xywhr format─▒nda zemin ger├že─či obb'lerini temsil eden (N, 5) ┼čeklinde bir tensor .

gerekli
obb2 Tensor

Tahmin edilen obb'leri temsil eden, xywhr format─▒nda bir tensor ┼čekli (N, 5).

gerekli
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
Tensor

Obb benzerliklerini temsil eden bir tensor ┼čekli (N, ).

Kaynak kodu ultralytics/utils/metrics.py
def probiou(obb1, obb2, CIoU=False, eps=1e-7):
    """
    Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.

    Args:
        obb1 (torch.Tensor): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
        obb2 (torch.Tensor): A tensor of shape (N, 5) representing predicted obbs, with xywhr format.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): A tensor of shape (N, ) representing obb similarities.
    """
    x1, y1 = obb1[..., :2].split(1, dim=-1)
    x2, y2 = obb2[..., :2].split(1, dim=-1)
    a1, b1, c1 = _get_covariance_matrix(obb1)
    a2, b2, c2 = _get_covariance_matrix(obb2)

    t1 = (
        ((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)
    ) * 0.25
    t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)) * 0.5
    t3 = (
        ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2))
        / (4 * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + eps)
        + eps
    ).log() * 0.5
    bd = (t1 + t2 + t3).clamp(eps, 100.0)
    hd = (1.0 - (-bd).exp() + eps).sqrt()
    iou = 1 - hd
    if CIoU:  # only include the wh aspect ratio part
        w1, h1 = obb1[..., 2:4].split(1, dim=-1)
        w2, h2 = obb2[..., 2:4].split(1, dim=-1)
        v = (4 / math.pi**2) * ((w2 / h2).atan() - (w1 / h1).atan()).pow(2)
        with torch.no_grad():
            alpha = v / (v - iou + (1 + eps))
        return iou - v * alpha  # CIoU
    return iou



ultralytics.utils.metrics.batch_probiou(obb1, obb2, eps=1e-07)

Y├Ânlendirilmi┼č s─▒n─▒rlay─▒c─▒ kutular aras─▒ndaki prob IoU'yu hesaplay─▒n, https://arxiv.org/pdf/2106.06072v1.pdf.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
obb1 Tensor | ndarray

xywhr format─▒nda zemin ger├že─či obb'lerini temsil eden (N, 5) ┼čeklinde bir tensor .

gerekli
obb2 Tensor | ndarray

Tahmin edilen obb'leri xywhr format─▒nda temsil eden bir tensor ┼čekli (M, 5).

gerekli
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-7'dir.

1e-07

─░ade:

Tip A├ž─▒klama
Tensor

Obb benzerliklerini temsil eden (N, M) ┼čeklindeki bir tensor .

Kaynak kodu ultralytics/utils/metrics.py
def batch_probiou(obb1, obb2, eps=1e-7):
    """
    Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.

    Args:
        obb1 (torch.Tensor | np.ndarray): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
        obb2 (torch.Tensor | np.ndarray): A tensor of shape (M, 5) representing predicted obbs, with xywhr format.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): A tensor of shape (N, M) representing obb similarities.
    """
    obb1 = torch.from_numpy(obb1) if isinstance(obb1, np.ndarray) else obb1
    obb2 = torch.from_numpy(obb2) if isinstance(obb2, np.ndarray) else obb2

    x1, y1 = obb1[..., :2].split(1, dim=-1)
    x2, y2 = (x.squeeze(-1)[None] for x in obb2[..., :2].split(1, dim=-1))
    a1, b1, c1 = _get_covariance_matrix(obb1)
    a2, b2, c2 = (x.squeeze(-1)[None] for x in _get_covariance_matrix(obb2))

    t1 = (
        ((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)
    ) * 0.25
    t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps)) * 0.5
    t3 = (
        ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2))
        / (4 * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + eps)
        + eps
    ).log() * 0.5
    bd = (t1 + t2 + t3).clamp(eps, 100.0)
    hd = (1.0 - (-bd).exp() + eps).sqrt()
    return 1 - hd



ultralytics.utils.metrics.smooth_BCE(eps=0.1)

D├╝zle┼čtirilmi┼č pozitif ve negatif ─░kili ├çapraz Entropi hedeflerini hesaplar.

Bu fonksiyon, verilen bir epsilon de─čerine dayal─▒ olarak pozitif ve negatif etiket yumu┼čatma BCE hedeflerini hesaplar. Uygulama ayr─▒nt─▒lar─▒ i├žin bkz. https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
eps float

Etiket yumu┼čatma i├žin epsilon de─čeri. Varsay─▒lan de─čer 0,1'dir.

0.1

─░ade:

Tip A├ž─▒klama
tuple

Pozitif ve negatif etiket yumu┼čatma BCE hedeflerini i├žeren bir tuple.

Kaynak kodu ultralytics/utils/metrics.py
def smooth_BCE(eps=0.1):
    """
    Computes smoothed positive and negative Binary Cross-Entropy targets.

    This function calculates positive and negative label smoothing BCE targets based on a given epsilon value.
    For implementation details, refer to https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441.

    Args:
        eps (float, optional): The epsilon value for label smoothing. Defaults to 0.1.

    Returns:
        (tuple): A tuple containing the positive and negative label smoothing BCE targets.
    """
    return 1.0 - 0.5 * eps, 0.5 * eps



ultralytics.utils.metrics.smooth(y, f=0.05)

Kesir f'nin kutu filtresi.

Kaynak kodu ultralytics/utils/metrics.py
def smooth(y, f=0.05):
    """Box filter of fraction f."""
    nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
    p = np.ones(nf // 2)  # ones padding
    yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
    return np.convolve(yp, np.ones(nf) / nf, mode="valid")  # y-smoothed



ultralytics.utils.metrics.plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=(), on_plot=None)

Hassasiyet-geri ├ža─č─▒rma e─črisini ├žizer.

Kaynak kodu ultralytics/utils/metrics.py
@plt_settings()
def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=(), on_plot=None):
    """Plots a precision-recall curve."""
    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
    py = np.stack(py, axis=1)

    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
        for i, y in enumerate(py.T):
            ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}")  # plot(recall, precision)
    else:
        ax.plot(px, py, linewidth=1, color="grey")  # plot(recall, precision)

    ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean())
    ax.set_xlabel("Recall")
    ax.set_ylabel("Precision")
    ax.set_xlim(0, 1)
    ax.set_ylim(0, 1)
    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
    ax.set_title("Precision-Recall Curve")
    fig.savefig(save_dir, dpi=250)
    plt.close(fig)
    if on_plot:
        on_plot(save_dir)



ultralytics.utils.metrics.plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric', on_plot=None)

Bir metrik-g├╝ven e─črisi ├žizer.

Kaynak kodu ultralytics/utils/metrics.py
@plt_settings()
def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric", on_plot=None):
    """Plots a metric-confidence curve."""
    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)

    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
        for i, y in enumerate(py):
            ax.plot(px, y, linewidth=1, label=f"{names[i]}")  # plot(confidence, metric)
    else:
        ax.plot(px, py.T, linewidth=1, color="grey")  # plot(confidence, metric)

    y = smooth(py.mean(0), 0.05)
    ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}")
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.set_xlim(0, 1)
    ax.set_ylim(0, 1)
    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
    ax.set_title(f"{ylabel}-Confidence Curve")
    fig.savefig(save_dir, dpi=250)
    plt.close(fig)
    if on_plot:
        on_plot(save_dir)



ultralytics.utils.metrics.compute_ap(recall, precision)

Geri ├ža─č─▒rma ve kesinlik e─črileri g├Âz ├Ân├╝ne al─▒nd─▒─č─▒nda ortalama kesinli─či (AP) hesaplay─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
recall list

Geri ├ža─č─▒rma e─črisi.

gerekli
precision list

Hassasiyet e─črisi.

gerekli

─░ade:

Tip A├ž─▒klama
float

Ortalama hassasiyet.

ndarray

Hassas zarf e─črisi.

ndarray

Ba┼č─▒na ve sonuna eklenen sentinel de─čerlerle de─či┼čtirilmi┼č geri ├ža─č─▒rma e─črisi.

Kaynak kodu ultralytics/utils/metrics.py
def compute_ap(recall, precision):
    """
    Compute the average precision (AP) given the recall and precision curves.

    Args:
        recall (list): The recall curve.
        precision (list): The precision curve.

    Returns:
        (float): Average precision.
        (np.ndarray): Precision envelope curve.
        (np.ndarray): Modified recall curve with sentinel values added at the beginning and end.
    """

    # Append sentinel values to beginning and end
    mrec = np.concatenate(([0.0], recall, [1.0]))
    mpre = np.concatenate(([1.0], precision, [0.0]))

    # Compute the precision envelope
    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))

    # Integrate area under curve
    method = "interp"  # methods: 'continuous', 'interp'
    if method == "interp":
        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
    else:  # 'continuous'
        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x-axis (recall) changes
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve

    return ap, mpre, mrec



ultralytics.utils.metrics.ap_per_class(tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names=(), eps=1e-16, prefix='')

Nesne alg─▒lama de─čerlendirmesi i├žin s─▒n─▒f ba┼č─▒na ortalama hassasiyeti hesaplar.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
tp ndarray

Alg─▒laman─▒n do─čru (True) veya yanl─▒┼č (False) oldu─čunu g├Âsteren ikili dizi.

gerekli
conf ndarray

Tespitlerin g├╝ven puanlar─▒ dizisi.

gerekli
pred_cls ndarray

Tespitlerin tahmin edilen s─▒n─▒flar─▒n─▒n dizisi.

gerekli
target_cls ndarray

Tespitlerin ger├žek s─▒n─▒flar─▒n─▒n dizisi.

gerekli
plot bool

PR e─črilerinin ├žizilip ├žizilmeyece─či. Varsay─▒lan de─čer False'dir.

False
on_plot func

Olu┼čturulduklar─▒nda ├žizim yolunu ve verileri iletmek i├žin bir geri arama. Varsay─▒lan de─čer Yok'tur.

None
save_dir Path

PR e─črilerinin kaydedilece─či dizin. Varsay─▒lan olarak bo┼č bir yoldur.

Path()
names tuple

PR e─črilerini ├žizmek i├žin s─▒n─▒f adlar─▒ demeti. Varsay─▒lan de─čer bo┼č bir tuple'd─▒r.

()
eps float

S─▒f─▒ra b├Âlmeyi ├Ânlemek i├žin k├╝├ž├╝k bir de─čer. Varsay─▒lan de─čer 1e-16'd─▒r.

1e-16
prefix str

├çizim dosyalar─▒n─▒ kaydetmek i├žin bir ├Ânek dizesi. Varsay─▒lan olarak bo┼č bir dizedir.

''

─░ade:

Tip A├ž─▒klama
tuple

Alt─▒ diziden ve bir benzersiz s─▒n─▒f dizisinden olu┼čan bir ├žift, burada: tp (np.ndarray): Her s─▒n─▒f i├žin maksimum F1 metri─či taraf─▒ndan verilen e┼čikteki ger├žek pozitif say─▒lar ┼×ekil: (nc,). fp (np.ndarray): Her s─▒n─▒f i├žin maksimum F1 metri─či taraf─▒ndan verilen e┼čikteki yanl─▒┼č pozitif say─▒lar─▒. ┼×ekil: (nc,). p (np.ndarray): Her s─▒n─▒f i├žin maksimum F1 metri─či taraf─▒ndan verilen e┼čikteki hassasiyet de─čerleri. ┼×ekil: (nc,). r (np.ndarray): Her s─▒n─▒f i├žin maksimum F1 metri─či taraf─▒ndan verilen e┼čikteki geri ├ža─č─▒rma de─čerleri. ┼×ekil: (nc,). f1 (np.ndarray): Her s─▒n─▒f i├žin maksimum F1 metri─či taraf─▒ndan verilen e┼čikteki F1 puan─▒ de─čerleri. ┼×ekil: (nc,). ap (np.ndarray): Farkl─▒ IoU e┼čiklerinde her s─▒n─▒f i├žin ortalama hassasiyet. ┼×ekil: (nc, 10). unique_classes (np.ndarray): Veriye sahip benzersiz s─▒n─▒flar─▒n bir dizisi. ┼×ekil: (nc,). p_curve (np.ndarray): Her s─▒n─▒f i├žin hassasiyet e─črileri. ┼×ekil: (nc, 1000). r_curve (np.ndarray): Her s─▒n─▒f i├žin geri ├ža─č─▒rma e─črileri. ┼×ekil: (nc, 1000). f1_curve (np.ndarray): Her s─▒n─▒f i├žin F1-skor e─črileri. ┼×ekil: (nc, 1000). x (np.ndarray): E─čriler i├žin X ekseni de─čerleri. ┼×ekil: (1000,). prec_values: Her s─▒n─▒f i├žin mAP@0.5 adresindeki hassasiyet de─čerleri. ┼×ekil: (nc, 1000).

Kaynak kodu ultralytics/utils/metrics.py
def ap_per_class(
    tp, conf, pred_cls, target_cls, plot=False, on_plot=None, save_dir=Path(), names=(), eps=1e-16, prefix=""
):
    """
    Computes the average precision per class for object detection evaluation.

    Args:
        tp (np.ndarray): Binary array indicating whether the detection is correct (True) or not (False).
        conf (np.ndarray): Array of confidence scores of the detections.
        pred_cls (np.ndarray): Array of predicted classes of the detections.
        target_cls (np.ndarray): Array of true classes of the detections.
        plot (bool, optional): Whether to plot PR curves or not. Defaults to False.
        on_plot (func, optional): A callback to pass plots path and data when they are rendered. Defaults to None.
        save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path.
        names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16.
        prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string.

    Returns:
        (tuple): A tuple of six arrays and one array of unique classes, where:
            tp (np.ndarray): True positive counts at threshold given by max F1 metric for each class.Shape: (nc,).
            fp (np.ndarray): False positive counts at threshold given by max F1 metric for each class. Shape: (nc,).
            p (np.ndarray): Precision values at threshold given by max F1 metric for each class. Shape: (nc,).
            r (np.ndarray): Recall values at threshold given by max F1 metric for each class. Shape: (nc,).
            f1 (np.ndarray): F1-score values at threshold given by max F1 metric for each class. Shape: (nc,).
            ap (np.ndarray): Average precision for each class at different IoU thresholds. Shape: (nc, 10).
            unique_classes (np.ndarray): An array of unique classes that have data. Shape: (nc,).
            p_curve (np.ndarray): Precision curves for each class. Shape: (nc, 1000).
            r_curve (np.ndarray): Recall curves for each class. Shape: (nc, 1000).
            f1_curve (np.ndarray): F1-score curves for each class. Shape: (nc, 1000).
            x (np.ndarray): X-axis values for the curves. Shape: (1000,).
            prec_values: Precision values at mAP@0.5 for each class. Shape: (nc, 1000).
    """

    # Sort by objectness
    i = np.argsort(-conf)
    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]

    # Find unique classes
    unique_classes, nt = np.unique(target_cls, return_counts=True)
    nc = unique_classes.shape[0]  # number of classes, number of detections

    # Create Precision-Recall curve and compute AP for each class
    x, prec_values = np.linspace(0, 1, 1000), []

    # Average precision, precision and recall curves
    ap, p_curve, r_curve = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
    for ci, c in enumerate(unique_classes):
        i = pred_cls == c
        n_l = nt[ci]  # number of labels
        n_p = i.sum()  # number of predictions
        if n_p == 0 or n_l == 0:
            continue

        # Accumulate FPs and TPs
        fpc = (1 - tp[i]).cumsum(0)
        tpc = tp[i].cumsum(0)

        # Recall
        recall = tpc / (n_l + eps)  # recall curve
        r_curve[ci] = np.interp(-x, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases

        # Precision
        precision = tpc / (tpc + fpc)  # precision curve
        p_curve[ci] = np.interp(-x, -conf[i], precision[:, 0], left=1)  # p at pr_score

        # AP from recall-precision curve
        for j in range(tp.shape[1]):
            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
            if plot and j == 0:
                prec_values.append(np.interp(x, mrec, mpre))  # precision at mAP@0.5

    prec_values = np.array(prec_values)  # (nc, 1000)

    # Compute F1 (harmonic mean of precision and recall)
    f1_curve = 2 * p_curve * r_curve / (p_curve + r_curve + eps)
    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
    names = dict(enumerate(names))  # to dict
    if plot:
        plot_pr_curve(x, prec_values, ap, save_dir / f"{prefix}PR_curve.png", names, on_plot=on_plot)
        plot_mc_curve(x, f1_curve, save_dir / f"{prefix}F1_curve.png", names, ylabel="F1", on_plot=on_plot)
        plot_mc_curve(x, p_curve, save_dir / f"{prefix}P_curve.png", names, ylabel="Precision", on_plot=on_plot)
        plot_mc_curve(x, r_curve, save_dir / f"{prefix}R_curve.png", names, ylabel="Recall", on_plot=on_plot)

    i = smooth(f1_curve.mean(0), 0.1).argmax()  # max F1 index
    p, r, f1 = p_curve[:, i], r_curve[:, i], f1_curve[:, i]  # max-F1 precision, recall, F1 values
    tp = (r * nt).round()  # true positives
    fp = (tp / (p + eps) - tp).round()  # false positives
    return tp, fp, p, r, f1, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (7), Burhan-Q (1), Laughing-q (1)