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Reference for ultralytics/data/base.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/base.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!



ultralytics.data.base.BaseDataset

Bases: Dataset

Base dataset class for loading and processing image data.

Parameters:

Name Type Description Default
img_path str

Path to the folder containing images.

required
imgsz int

Image size. Defaults to 640.

640
cache bool

Cache images to RAM or disk during training. Defaults to False.

False
augment bool

If True, data augmentation is applied. Defaults to True.

True
hyp dict

Hyperparameters to apply data augmentation. Defaults to None.

DEFAULT_CFG
prefix str

Prefix to print in log messages. Defaults to ''.

''
rect bool

If True, rectangular training is used. Defaults to False.

False
batch_size int

Size of batches. Defaults to None.

16
stride int

Stride. Defaults to 32.

32
pad float

Padding. Defaults to 0.0.

0.5
single_cls bool

If True, single class training is used. Defaults to False.

False
classes list

List of included classes. Default is None.

None
fraction float

Fraction of dataset to utilize. Default is 1.0 (use all data).

1.0

Attributes:

Name Type Description
im_files list

List of image file paths.

labels list

List of label data dictionaries.

ni int

Number of images in the dataset.

ims list

List of loaded images.

npy_files list

List of numpy file paths.

transforms callable

Image transformation function.

Source code in ultralytics/data/base.py
class BaseDataset(Dataset):
    """
    Base dataset class for loading and processing image data.

    Args:
        img_path (str): Path to the folder containing images.
        imgsz (int, optional): Image size. Defaults to 640.
        cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
        augment (bool, optional): If True, data augmentation is applied. Defaults to True.
        hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
        prefix (str, optional): Prefix to print in log messages. Defaults to ''.
        rect (bool, optional): If True, rectangular training is used. Defaults to False.
        batch_size (int, optional): Size of batches. Defaults to None.
        stride (int, optional): Stride. Defaults to 32.
        pad (float, optional): Padding. Defaults to 0.0.
        single_cls (bool, optional): If True, single class training is used. Defaults to False.
        classes (list): List of included classes. Default is None.
        fraction (float): Fraction of dataset to utilize. Default is 1.0 (use all data).

    Attributes:
        im_files (list): List of image file paths.
        labels (list): List of label data dictionaries.
        ni (int): Number of images in the dataset.
        ims (list): List of loaded images.
        npy_files (list): List of numpy file paths.
        transforms (callable): Image transformation function.
    """

    def __init__(
        self,
        img_path,
        imgsz=640,
        cache=False,
        augment=True,
        hyp=DEFAULT_CFG,
        prefix="",
        rect=False,
        batch_size=16,
        stride=32,
        pad=0.5,
        single_cls=False,
        classes=None,
        fraction=1.0,
    ):
        """Initialize BaseDataset with given configuration and options."""
        super().__init__()
        self.img_path = img_path
        self.imgsz = imgsz
        self.augment = augment
        self.single_cls = single_cls
        self.prefix = prefix
        self.fraction = fraction
        self.im_files = self.get_img_files(self.img_path)
        self.labels = self.get_labels()
        self.update_labels(include_class=classes)  # single_cls and include_class
        self.ni = len(self.labels)  # number of images
        self.rect = rect
        self.batch_size = batch_size
        self.stride = stride
        self.pad = pad
        if self.rect:
            assert self.batch_size is not None
            self.set_rectangle()

        # Buffer thread for mosaic images
        self.buffer = []  # buffer size = batch size
        self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0

        # Cache images
        if cache == "ram" and not self.check_cache_ram():
            cache = False
        self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni
        self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
        if cache:
            self.cache_images(cache)

        # Transforms
        self.transforms = self.build_transforms(hyp=hyp)

    def get_img_files(self, img_path):
        """Read image files."""
        try:
            f = []  # image files
            for p in img_path if isinstance(img_path, list) else [img_path]:
                p = Path(p)  # os-agnostic
                if p.is_dir():  # dir
                    f += glob.glob(str(p / "**" / "*.*"), recursive=True)
                    # F = list(p.rglob('*.*'))  # pathlib
                elif p.is_file():  # file
                    with open(p) as t:
                        t = t.read().strip().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace("./", parent) if x.startswith("./") else x for x in t]  # local to global path
                        # F += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
                else:
                    raise FileNotFoundError(f"{self.prefix}{p} does not exist")
            im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
            assert im_files, f"{self.prefix}No images found in {img_path}"
        except Exception as e:
            raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
        if self.fraction < 1:
            im_files = im_files[: round(len(im_files) * self.fraction)]
        return im_files

    def update_labels(self, include_class: Optional[list]):
        """Update labels to include only these classes (optional)."""
        include_class_array = np.array(include_class).reshape(1, -1)
        for i in range(len(self.labels)):
            if include_class is not None:
                cls = self.labels[i]["cls"]
                bboxes = self.labels[i]["bboxes"]
                segments = self.labels[i]["segments"]
                keypoints = self.labels[i]["keypoints"]
                j = (cls == include_class_array).any(1)
                self.labels[i]["cls"] = cls[j]
                self.labels[i]["bboxes"] = bboxes[j]
                if segments:
                    self.labels[i]["segments"] = [segments[si] for si, idx in enumerate(j) if idx]
                if keypoints is not None:
                    self.labels[i]["keypoints"] = keypoints[j]
            if self.single_cls:
                self.labels[i]["cls"][:, 0] = 0

    def load_image(self, i, rect_mode=True):
        """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
        if im is None:  # not cached in RAM
            if fn.exists():  # load npy
                try:
                    im = np.load(fn)
                except Exception as e:
                    LOGGER.warning(f"{self.prefix}WARNING ⚠️ Removing corrupt *.npy image file {fn} due to: {e}")
                    Path(fn).unlink(missing_ok=True)
                    im = cv2.imread(f)  # BGR
            else:  # read image
                im = cv2.imread(f)  # BGR
            if im is None:
                raise FileNotFoundError(f"Image Not Found {f}")

            h0, w0 = im.shape[:2]  # orig hw
            if rect_mode:  # resize long side to imgsz while maintaining aspect ratio
                r = self.imgsz / max(h0, w0)  # ratio
                if r != 1:  # if sizes are not equal
                    w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
                    im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
            elif not (h0 == w0 == self.imgsz):  # resize by stretching image to square imgsz
                im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)

            # Add to buffer if training with augmentations
            if self.augment:
                self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
                self.buffer.append(i)
                if len(self.buffer) >= self.max_buffer_length:
                    j = self.buffer.pop(0)
                    self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None

            return im, (h0, w0), im.shape[:2]

        return self.ims[i], self.im_hw0[i], self.im_hw[i]

    def cache_images(self, cache):
        """Cache images to memory or disk."""
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        fcn = self.cache_images_to_disk if cache == "disk" else self.load_image
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(fcn, range(self.ni))
            pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
            for i, x in pbar:
                if cache == "disk":
                    b += self.npy_files[i].stat().st_size
                else:  # 'ram'
                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                    b += self.ims[i].nbytes
                pbar.desc = f"{self.prefix}Caching images ({b / gb:.1f}GB {cache})"
            pbar.close()

    def cache_images_to_disk(self, i):
        """Saves an image as an *.npy file for faster loading."""
        f = self.npy_files[i]
        if not f.exists():
            np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)

    def check_cache_ram(self, safety_margin=0.5):
        """Check image caching requirements vs available memory."""
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        n = min(self.ni, 30)  # extrapolate from 30 random images
        for _ in range(n):
            im = cv2.imread(random.choice(self.im_files))  # sample image
            ratio = self.imgsz / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
            b += im.nbytes * ratio**2
        mem_required = b * self.ni / n * (1 + safety_margin)  # GB required to cache dataset into RAM
        mem = psutil.virtual_memory()
        cache = mem_required < mem.available  # to cache or not to cache, that is the question
        if not cache:
            LOGGER.info(
                f'{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images '
                f'with {int(safety_margin * 100)}% safety margin but only '
                f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
                f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
            )
        return cache

    def set_rectangle(self):
        """Sets the shape of bounding boxes for YOLO detections as rectangles."""
        bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches

        s = np.array([x.pop("shape") for x in self.labels])  # hw
        ar = s[:, 0] / s[:, 1]  # aspect ratio
        irect = ar.argsort()
        self.im_files = [self.im_files[i] for i in irect]
        self.labels = [self.labels[i] for i in irect]
        ar = ar[irect]

        # Set training image shapes
        shapes = [[1, 1]] * nb
        for i in range(nb):
            ari = ar[bi == i]
            mini, maxi = ari.min(), ari.max()
            if maxi < 1:
                shapes[i] = [maxi, 1]
            elif mini > 1:
                shapes[i] = [1, 1 / mini]

        self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
        self.batch = bi  # batch index of image

    def __getitem__(self, index):
        """Returns transformed label information for given index."""
        return self.transforms(self.get_image_and_label(index))

    def get_image_and_label(self, index):
        """Get and return label information from the dataset."""
        label = deepcopy(self.labels[index])  # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
        label.pop("shape", None)  # shape is for rect, remove it
        label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
        label["ratio_pad"] = (
            label["resized_shape"][0] / label["ori_shape"][0],
            label["resized_shape"][1] / label["ori_shape"][1],
        )  # for evaluation
        if self.rect:
            label["rect_shape"] = self.batch_shapes[self.batch[index]]
        return self.update_labels_info(label)

    def __len__(self):
        """Returns the length of the labels list for the dataset."""
        return len(self.labels)

    def update_labels_info(self, label):
        """Custom your label format here."""
        return label

    def build_transforms(self, hyp=None):
        """
        Users can customize augmentations here.

        Example:
            ```python
            if self.augment:
                # Training transforms
                return Compose([])
            else:
                # Val transforms
                return Compose([])
            ```
        """
        raise NotImplementedError

    def get_labels(self):
        """
        Users can customize their own format here.

        Note:
            Ensure output is a dictionary with the following keys:
            ```python
            dict(
                im_file=im_file,
                shape=shape,  # format: (height, width)
                cls=cls,
                bboxes=bboxes, # xywh
                segments=segments,  # xy
                keypoints=keypoints, # xy
                normalized=True, # or False
                bbox_format="xyxy",  # or xywh, ltwh
            )
            ```
        """
        raise NotImplementedError

__getitem__(index)

Returns transformed label information for given index.

Source code in ultralytics/data/base.py
def __getitem__(self, index):
    """Returns transformed label information for given index."""
    return self.transforms(self.get_image_and_label(index))

__init__(img_path, imgsz=640, cache=False, augment=True, hyp=DEFAULT_CFG, prefix='', rect=False, batch_size=16, stride=32, pad=0.5, single_cls=False, classes=None, fraction=1.0)

Initialize BaseDataset with given configuration and options.

Source code in ultralytics/data/base.py
def __init__(
    self,
    img_path,
    imgsz=640,
    cache=False,
    augment=True,
    hyp=DEFAULT_CFG,
    prefix="",
    rect=False,
    batch_size=16,
    stride=32,
    pad=0.5,
    single_cls=False,
    classes=None,
    fraction=1.0,
):
    """Initialize BaseDataset with given configuration and options."""
    super().__init__()
    self.img_path = img_path
    self.imgsz = imgsz
    self.augment = augment
    self.single_cls = single_cls
    self.prefix = prefix
    self.fraction = fraction
    self.im_files = self.get_img_files(self.img_path)
    self.labels = self.get_labels()
    self.update_labels(include_class=classes)  # single_cls and include_class
    self.ni = len(self.labels)  # number of images
    self.rect = rect
    self.batch_size = batch_size
    self.stride = stride
    self.pad = pad
    if self.rect:
        assert self.batch_size is not None
        self.set_rectangle()

    # Buffer thread for mosaic images
    self.buffer = []  # buffer size = batch size
    self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0

    # Cache images
    if cache == "ram" and not self.check_cache_ram():
        cache = False
    self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni
    self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
    if cache:
        self.cache_images(cache)

    # Transforms
    self.transforms = self.build_transforms(hyp=hyp)

__len__()

Returns the length of the labels list for the dataset.

Source code in ultralytics/data/base.py
def __len__(self):
    """Returns the length of the labels list for the dataset."""
    return len(self.labels)

build_transforms(hyp=None)

Users can customize augmentations here.

Example
if self.augment:
    # Training transforms
    return Compose([])
else:
    # Val transforms
    return Compose([])
Source code in ultralytics/data/base.py
def build_transforms(self, hyp=None):
    """
    Users can customize augmentations here.

    Example:
        ```python
        if self.augment:
            # Training transforms
            return Compose([])
        else:
            # Val transforms
            return Compose([])
        ```
    """
    raise NotImplementedError

cache_images(cache)

Cache images to memory or disk.

Source code in ultralytics/data/base.py
def cache_images(self, cache):
    """Cache images to memory or disk."""
    b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
    fcn = self.cache_images_to_disk if cache == "disk" else self.load_image
    with ThreadPool(NUM_THREADS) as pool:
        results = pool.imap(fcn, range(self.ni))
        pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
        for i, x in pbar:
            if cache == "disk":
                b += self.npy_files[i].stat().st_size
            else:  # 'ram'
                self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                b += self.ims[i].nbytes
            pbar.desc = f"{self.prefix}Caching images ({b / gb:.1f}GB {cache})"
        pbar.close()

cache_images_to_disk(i)

Saves an image as an *.npy file for faster loading.

Source code in ultralytics/data/base.py
def cache_images_to_disk(self, i):
    """Saves an image as an *.npy file for faster loading."""
    f = self.npy_files[i]
    if not f.exists():
        np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)

check_cache_ram(safety_margin=0.5)

Check image caching requirements vs available memory.

Source code in ultralytics/data/base.py
def check_cache_ram(self, safety_margin=0.5):
    """Check image caching requirements vs available memory."""
    b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
    n = min(self.ni, 30)  # extrapolate from 30 random images
    for _ in range(n):
        im = cv2.imread(random.choice(self.im_files))  # sample image
        ratio = self.imgsz / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
        b += im.nbytes * ratio**2
    mem_required = b * self.ni / n * (1 + safety_margin)  # GB required to cache dataset into RAM
    mem = psutil.virtual_memory()
    cache = mem_required < mem.available  # to cache or not to cache, that is the question
    if not cache:
        LOGGER.info(
            f'{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images '
            f'with {int(safety_margin * 100)}% safety margin but only '
            f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
            f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
        )
    return cache

get_image_and_label(index)

Get and return label information from the dataset.

Source code in ultralytics/data/base.py
def get_image_and_label(self, index):
    """Get and return label information from the dataset."""
    label = deepcopy(self.labels[index])  # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
    label.pop("shape", None)  # shape is for rect, remove it
    label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
    label["ratio_pad"] = (
        label["resized_shape"][0] / label["ori_shape"][0],
        label["resized_shape"][1] / label["ori_shape"][1],
    )  # for evaluation
    if self.rect:
        label["rect_shape"] = self.batch_shapes[self.batch[index]]
    return self.update_labels_info(label)

get_img_files(img_path)

Read image files.

Source code in ultralytics/data/base.py
def get_img_files(self, img_path):
    """Read image files."""
    try:
        f = []  # image files
        for p in img_path if isinstance(img_path, list) else [img_path]:
            p = Path(p)  # os-agnostic
            if p.is_dir():  # dir
                f += glob.glob(str(p / "**" / "*.*"), recursive=True)
                # F = list(p.rglob('*.*'))  # pathlib
            elif p.is_file():  # file
                with open(p) as t:
                    t = t.read().strip().splitlines()
                    parent = str(p.parent) + os.sep
                    f += [x.replace("./", parent) if x.startswith("./") else x for x in t]  # local to global path
                    # F += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
            else:
                raise FileNotFoundError(f"{self.prefix}{p} does not exist")
        im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
        # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
        assert im_files, f"{self.prefix}No images found in {img_path}"
    except Exception as e:
        raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
    if self.fraction < 1:
        im_files = im_files[: round(len(im_files) * self.fraction)]
    return im_files

get_labels()

Users can customize their own format here.

Note

Ensure output is a dictionary with the following keys:

dict(
    im_file=im_file,
    shape=shape,  # format: (height, width)
    cls=cls,
    bboxes=bboxes, # xywh
    segments=segments,  # xy
    keypoints=keypoints, # xy
    normalized=True, # or False
    bbox_format="xyxy",  # or xywh, ltwh
)

Source code in ultralytics/data/base.py
def get_labels(self):
    """
    Users can customize their own format here.

    Note:
        Ensure output is a dictionary with the following keys:
        ```python
        dict(
            im_file=im_file,
            shape=shape,  # format: (height, width)
            cls=cls,
            bboxes=bboxes, # xywh
            segments=segments,  # xy
            keypoints=keypoints, # xy
            normalized=True, # or False
            bbox_format="xyxy",  # or xywh, ltwh
        )
        ```
    """
    raise NotImplementedError

load_image(i, rect_mode=True)

Loads 1 image from dataset index 'i', returns (im, resized hw).

Source code in ultralytics/data/base.py
def load_image(self, i, rect_mode=True):
    """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
    im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
    if im is None:  # not cached in RAM
        if fn.exists():  # load npy
            try:
                im = np.load(fn)
            except Exception as e:
                LOGGER.warning(f"{self.prefix}WARNING ⚠️ Removing corrupt *.npy image file {fn} due to: {e}")
                Path(fn).unlink(missing_ok=True)
                im = cv2.imread(f)  # BGR
        else:  # read image
            im = cv2.imread(f)  # BGR
        if im is None:
            raise FileNotFoundError(f"Image Not Found {f}")

        h0, w0 = im.shape[:2]  # orig hw
        if rect_mode:  # resize long side to imgsz while maintaining aspect ratio
            r = self.imgsz / max(h0, w0)  # ratio
            if r != 1:  # if sizes are not equal
                w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
                im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
        elif not (h0 == w0 == self.imgsz):  # resize by stretching image to square imgsz
            im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)

        # Add to buffer if training with augmentations
        if self.augment:
            self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
            self.buffer.append(i)
            if len(self.buffer) >= self.max_buffer_length:
                j = self.buffer.pop(0)
                self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None

        return im, (h0, w0), im.shape[:2]

    return self.ims[i], self.im_hw0[i], self.im_hw[i]

set_rectangle()

Sets the shape of bounding boxes for YOLO detections as rectangles.

Source code in ultralytics/data/base.py
def set_rectangle(self):
    """Sets the shape of bounding boxes for YOLO detections as rectangles."""
    bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
    nb = bi[-1] + 1  # number of batches

    s = np.array([x.pop("shape") for x in self.labels])  # hw
    ar = s[:, 0] / s[:, 1]  # aspect ratio
    irect = ar.argsort()
    self.im_files = [self.im_files[i] for i in irect]
    self.labels = [self.labels[i] for i in irect]
    ar = ar[irect]

    # Set training image shapes
    shapes = [[1, 1]] * nb
    for i in range(nb):
        ari = ar[bi == i]
        mini, maxi = ari.min(), ari.max()
        if maxi < 1:
            shapes[i] = [maxi, 1]
        elif mini > 1:
            shapes[i] = [1, 1 / mini]

    self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
    self.batch = bi  # batch index of image

update_labels(include_class)

Update labels to include only these classes (optional).

Source code in ultralytics/data/base.py
def update_labels(self, include_class: Optional[list]):
    """Update labels to include only these classes (optional)."""
    include_class_array = np.array(include_class).reshape(1, -1)
    for i in range(len(self.labels)):
        if include_class is not None:
            cls = self.labels[i]["cls"]
            bboxes = self.labels[i]["bboxes"]
            segments = self.labels[i]["segments"]
            keypoints = self.labels[i]["keypoints"]
            j = (cls == include_class_array).any(1)
            self.labels[i]["cls"] = cls[j]
            self.labels[i]["bboxes"] = bboxes[j]
            if segments:
                self.labels[i]["segments"] = [segments[si] for si, idx in enumerate(j) if idx]
            if keypoints is not None:
                self.labels[i]["keypoints"] = keypoints[j]
        if self.single_cls:
            self.labels[i]["cls"][:, 0] = 0

update_labels_info(label)

Custom your label format here.

Source code in ultralytics/data/base.py
def update_labels_info(self, label):
    """Custom your label format here."""
    return label





Created 2023-11-12, Updated 2023-11-25
Authors: glenn-jocher (3)