numpy._core._exceptions._ArrayMemoryError: Unable to allocate 11.9 MiB for an array with shape (6, 1080, 1920) and data type bool

[Jarradmorden]

Search before asking

• I have searched the Roboflow Notebooks issues and found no similar bug report.

Notebook name

https://github.com/roboflow/notebooks/blob/main/notebooks/how-to-auto-train-yolov8-model-with-autodistill.ipynb

Bug

Hello,

I am following some of the tutorials that roboflow offers, I am doing a custom dataset I have over 9000 pictures and I am using the ontology, it would seem when I reach the end of my training I run into memory issues "numpy._core._exceptions._ArrayMemoryError: Unable to allocate 11.9 MiB for an array with shape (6, 1080, 1920) and data type bool" and it only works if I train with a much smaller sample, I am guessing this is because they are all being processed in one go, I followed the tutorial for making a custom dataset but I think this would happen to anyone with a much larger size. How can I get around this issue.

I understand lowering the resolution would help but still by having so many issues I face the same issue, how would i go around this?

I put this as a bug because I am not sure if this notebook accounts for very large datasets so it would be good to do that if it's something that can happen to anyone, thank you

#####ISSUE########

left_9_2024-10-18 15-14-24.770364.png: 100%|██████████████████████████████████| 12498/12498 [6:07:58<00:00, 1.77s/it]
Passing a Dict[str, np.ndarray] into DetectionDataset is deprecated and will be removed in supervision-0.26.0. Use a list of paths List[str] instead.
Found dataset\train\images\left_9339_2024-10-18 15-23-39.366113.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_9339_2024-10-18 15-23-39.366113.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_4584_2024-10-18 15-18-59.357296.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_4584_2024-10-18 15-18-59.357296.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_4583_2024-10-18 15-18-59.323971.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_4583_2024-10-18 15-18-59.323971.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_3070_2024-10-18 15-17-30.481540.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_3070_2024-10-18 15-17-30.481540.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_9338_2024-10-18 15-23-39.299582.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_9338_2024-10-18 15-23-39.299582.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_9335_2024-10-18 15-23-39.131904.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_9335_2024-10-18 15-23-39.131904.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_9336_2024-10-18 15-23-39.198825.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_9336_2024-10-18 15-23-39.198825.txt as already present, not moving anything to dataset\train\labels
Found dataset\train\images\left_9337_2024-10-18 15-23-39.265686.jpg as already present, not moving anything to dataset\train\images
Found dataset\train\labels\left_9337_2024-10-18 15-23-39.265686.txt as already present, not moving anything to dataset\train\labels
Labeled dataset created - ready for distillation.
Traceback (most recent call last):
File "c:\Users\xxxxxx\DATA\model.py", line 45, in
dataset = sv.DetectionDataset.from_yolo(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\Usersxxxx\AppData\Local\anaconda3\envs\visper_environment\Lib\site-packages\supervision\dataset\core.py", line 497, in from_yolo
classes, image_paths, annotations = load_yolo_annotations(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "cxxxxxx\AppData\Local\anaconda3\envs\xxxx_environment\Lib\site-packages\supervision\dataset\formats\yolo.py", line 120, in yolo_annotations_to_detections
mask = _polygons_to_masks(polygons=polygons, resolution_wh=resolution_wh)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\Users\xxxxxx\AppData\Local\anaconda3\envs\xxx_environment\Lib\site-packages\supervision\dataset\formats\yolo.py", line 50, in _polygons_to_masks
return np.array(
^^^^^^^^^
numpy._core._exceptions._ArrayMemoryError: Unable to allocate 11.9 MiB for an array with shape (6, 1080, 1920) and data type bool

####MY CODE#####

import os
import torch  # Ensure torch is available for CUDA checks
from autodistill_grounded_sam import GroundedSAM
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
import cv2
import supervision as sv
from autodistill_yolov8 import YOLOv8

IMAGE_DIR_PATH = f"2024-10-18_15-13-59/left_images"
DATASET_DIR_PATH = f"dataset"
ANNOTATIONS_DIRECTORY_PATH = f"dataset/train/labels"
IMAGES_DIRECTORY_PATH = f"dataset/train/images"
DATA_YAML_PATH = f"dataset/data.yaml"

image_paths = sv.list_files_with_extensions(
    directory=IMAGE_DIR_PATH,
    extensions=["png", "jpg", "jpg"]
)
print('image count:', len(image_paths))

ontology = CaptionOntology({
    "rear end of car": "Rear Car",
    "back view of vehicle": "Rear Car",
    "trunk of car": "Rear Car",
    "number plate": "licence plate"
})

device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Training will use: {device.upper()}")

base_model = GroundedSAM(ontology=ontology)
dataset = base_model.label(
    input_folder=IMAGE_DIR_PATH,
    extension=".png",
    output_folder=DATASET_DIR_PATH
)

dataset = sv.DetectionDataset.from_yolo(
    images_directory_path=IMAGES_DIRECTORY_PATH,
    annotations_directory_path=ANNOTATIONS_DIRECTORY_PATH,
    data_yaml_path=DATA_YAML_PATH
)

SAMPLE_SIZE = 16
SAMPLE_GRID_SIZE = (4, 4)
SAMPLE_PLOT_SIZE = (16, 16)

mask_annotator = sv.MaskAnnotator()
box_annotator = sv.BoxAnnotator()
image = cv2.imread(f"xxxxxxx/DATA/2024-10-18_15-13-59/sample/left_4583_2024-10-18 15-18-59.323971.png")


images = []
image_names = list(dataset.images.keys())[:SAMPLE_SIZE]
for image_name in image_names:
    image = dataset.images[image_name]
    annotations = dataset.annotations[image_name]
    labels = [dataset.classes[class_id] for class_id in annotations.class_id]
    
    annotated_image = mask_annotator.annotate(scene=image.copy(), detections=annotations)
    annotated_image = box_annotator.annotate(scene=annotated_image, detections=annotations)
    images.append(annotated_image)

sv.plot_images_grid(
    images=images,
    titles=image_names,
    grid_size=SAMPLE_GRID_SIZE,
    size=SAMPLE_PLOT_SIZE
)
sv.plot_image(image=annotated_image, size=(8, 8))

target_model = YOLOv8("yolov8s-seg.pt")
target_model.train(DATA_YAML_PATH, epochs=2, device=device)

Windows 11

I have quite good specs too

python 3.11
and NVIDEA RTX A5000 Graphics card

[LinasKo]

Hi @Jarradmorden 👋

Thank you for the report. Indeed, the notebook is a bit behind, but more importantly, Autodistill hasn't been updated to handle large datasets.

I'll have a look at both this evening. Most likely I can bring it up to speed.

[Jarradmorden]

Hi @Jarradmorden 👋

Thank you for the report. Indeed, the notebook is a bit behind, but more importantly, Autodistill hasn't been updated to handle large datasets.

I'll have a look at both this evening. Most likely I can bring it up to speed.

Thanks that would be great!

[LinasKo]

The notebook now uses the latest supervision, and the dataset is loaded lazily.

Depending on the part where you ran out of RAM previously, this time it could work. Notably, a high-ram operation is calling dataset.images, which the new code avoids.

https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-auto-train-yolov8-model-with-autodistill.ipynb

It might be enough, and Autodistill won't need any changes.

[Jarradmorden]

Awesome thanks! Very swift and speedy I will let you know if it works before accepting answer =]

[Jarradmorden]

To get back over to which part was asking for an extreme amount of RAM was the distillation, it gets to 100% and then the labels are created and the distillation takes forever and never ends and crashes before finishing. Do you know which part specifically in your code fixes this, I have been trying to see the difference of the first one and last one but it says the file is too large, was it many changes or a specific function, thanks!

basically just after this part

base_model = GroundedSAM(ontology=ontology)
dataset = base_model.label(
input_folder=IMAGE_DIR_PATH,
extension=".png",
output_folder=DATASET_DIR_PATH)

FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
trying to load grounding dino directly
downloading dino model weights
UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3595.)
final text_encoder_type: bert-base-uncased
tokenizer_config.json: 100%
 48.0/48.0 [00:00<00:00, 4.11kB/s]
config.json: 100%
 570/570 [00:00<00:00, 51.9kB/s]
vocab.txt: 100%
 232k/232k [00:00<00:00, 1.80MB/s]
tokenizer.json: 100%
 466k/466k [00:00<00:00, 2.48MB/s]
FutureWarning: clean_up_tokenization_spaces was not set. It will be set to True by default. This behavior will be depracted in transformers v4.45, and will be then set to False by default. For more details check this issue: huggingface/transformers#31884
model.safetensors: 100%
 440M/440M [00:01<00:00, 245MB/s]
FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
Labeling /content/images/milk-video-8-00021.png: 0%| | 0/153 [00:00<?, ?it/s]FutureWarning: The device argument is deprecated and will be removed in v5 of Transformers.
UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
UserWarning: None of the inputs have requires_grad=True. Gradients will be None
FutureWarning: torch.cuda.amp.autocast(args...) is deprecated. Please use torch.amp.autocast('cuda', args...) instead.
FutureWarning: The device argument is deprecated and will be removed in v5 of Transformers.
UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
UserWarning: None of the inputs have requires_grad=True. Gradients will be None
FutureWarning: torch.cuda.amp.autocast(args...) is deprecated. Please use torch.amp.autocast('cuda', args...) instead.
Labeling /content/images/milk-video-5-00004.png: 100%|██████████| 153/153 [08:55<00:00, 3.50s/it]
SupervisionWarnings: Passing a Dict[str, np.ndarray]

[LinasKo]

I fixed a later part. Looks like an autodistill issue; guess I should try fixing that too

[Jarradmorden]

I fixed a later part. Looks like an autodistill issue; guess I should try fixing that too

Thanks, I will keep a look out :)

[LinasKo]

Hi @Jarradmorden 👋

I had commented on the wrong issue. There's a branch on autodistill I created yesterday, that introduces lazy dataset loading.

Would you mind checking if it makes a difference?

pip install git+https://github.com/autodistill/autodistill.git@feat/supervision-0.24.0-support

[Jarradmorden]

thats perfect thanks I will get back to you today or tomorrow thank you so much!

[Jarradmorden]

Hi @Jarradmorden 👋

I had commented on the wrong issue. There's a branch on autodistill I created yesterday, that introduces lazy dataset loading.

Would you mind checking if it makes a difference?

pip install git+https://github.com/autodistill/autodistill.git@feat/supervision-0.24.0-support

Hi Do you know which branch i should check I am getting issues when loading my images with the path

def load_images(self):
    # Label the images and prepare the dataset exactly like the original script
    self.dataset = self.base_model.label(
        input_folder=IMAGE_DIR_PATH,
        extension=".jpeg",
        output_folder=DATASET_DIR_PATH
    )
    self.image_names = list(self.dataset.images.keys())
    self.total_pages = (len(self.image_names) + 3) // 4
    self.annotate_images()

is this what i should be changing? I could not notice the changes on the notebook, Thanks!

[LinasKo]

The branch name of autodistill is feat/supervision-0.24.0-support.
I made my changes here:autodistill/autodistill#180

I expect autodistill to work as before, and when taken together with my proposed changes of the Colab notebook, the memory usage should be much lower.

The main change is this: Previously dataset would need all images loaded to memory for a dataset to be created. Now, internally autodistill only uses image paths, and if needed, loads one image to memory temporarily. However, this is only true if you don't call dataset.images, as that will load everything into RAM once again.

To avoid high RAM usage, use the autodistill branch proided, and in your code use the loop for image_path, image, annotations in dataset: instead of calling dataset.images.

[Jarradmorden]

hey!

been stuck on this for a few days ever since i installed the pip support package It no longer finds the paths of the files
Please check out the load function which is causing the issue specifically

def load_images(self):
    # Label the images and prepare the dataset
    self.dataset = self.base_model.label(
        input_folder=IMAGE_DIR_PATH,
        extension=".jpeg",
        output_folder=DATASET_DIR_PATH
    )

    # Set total pages based on the dataset length
    self.total_pages = (len(self.dataset) + 3) // 4
    self.current_page_index = 0  # Start at the first page
    self.annotate_images()

my code ###

import sys
import os
import shutil
import cv2
from PySide6 import QtWidgets, QtGui, QtCore
from autodistill_grounded_sam import GroundedSAM
from autodistill.detection import CaptionOntology
import supervision as sv
from autodistill_yolov8 import YOLOv8

# Paths for images, labels, and dataset
IMAGE_DIR_PATH = "data"
DATASET_DIR_PATH = "dataset"
TRAIN_IMAGES_DIRECTORY = os.path.join(DATASET_DIR_PATH, "train/images")
TRAIN_LABELS_DIRECTORY = os.path.join(DATASET_DIR_PATH, "train/labels")
VALID_IMAGES_DIRECTORY = os.path.join(DATASET_DIR_PATH, "valid/images")
VALID_LABELS_DIRECTORY = os.path.join(DATASET_DIR_PATH, "valid/labels")
DATA_YAML_PATH = os.path.join(DATASET_DIR_PATH, "data.yaml")

class ImageAnnotatorApp(QtWidgets.QWidget):
    def __init__(self):
        super().__init__()
        self.initUI()

        # Define ontology and base model
        self.ontology = CaptionOntology({
            "back of bananas": "back of bananas",
            "front of bananas": "front of bananas",
        })
        self.base_model = GroundedSAM(ontology=self.ontology)
        self.dataset = None
        self.current_page_index = 0
        self.image_status = {}

        # Clear any existing train and valid directories at the start to ensure a clean state
        self.clear_directory(TRAIN_IMAGES_DIRECTORY)
        self.clear_directory(TRAIN_LABELS_DIRECTORY)
        self.clear_directory(VALID_IMAGES_DIRECTORY)
        self.clear_directory(VALID_LABELS_DIRECTORY)

    def initUI(self):
        self.setWindowTitle('Image Annotator')
        self.setMinimumSize(1000, 800)
        self.resize(1200, 900)

        self.pageLabel = QtWidgets.QLabel(self)
        self.pageLabel.setAlignment(QtCore.Qt.AlignCenter)
        self.statusLabels = [QtWidgets.QLabel("Unreviewed", self) for _ in range(4)]
        for status_label in self.statusLabels:
            status_label.setAlignment(QtCore.Qt.AlignCenter)

        self.gridLayout = QtWidgets.QGridLayout()
        self.imageLabels = [QtWidgets.QLabel(self) for _ in range(4)]
        self.rejectButtons = [QtWidgets.QCheckBox("Reject", self) for _ in range(4)]
        
        for i, label in enumerate(self.imageLabels):
            label.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
            label.setStyleSheet("border: 1px solid black;")
            label.setAlignment(QtCore.Qt.AlignCenter)
            row, col = divmod(i, 2)
            self.gridLayout.addWidget(label, row * 3, col)
            self.gridLayout.addWidget(self.rejectButtons[i], row * 3 + 1, col)
            self.gridLayout.addWidget(self.statusLabels[i], row * 3 + 2, col)

        control_layout = QtWidgets.QHBoxLayout()
        self.loadButton = QtWidgets.QPushButton('Load Images', self)
        self.loadButton.clicked.connect(self.load_images)
        self.prevButton = QtWidgets.QPushButton('Previous Page', self)
        self.prevButton.clicked.connect(self.prev_page)
        self.nextButton = QtWidgets.QPushButton('Next Page', self)
        self.nextButton.clicked.connect(self.next_page)
        self.acceptButton = QtWidgets.QPushButton('Accept Page', self)
        self.acceptButton.clicked.connect(self.accept_page)
        self.splitButton = QtWidgets.QPushButton('Create Validation Split', self)
        self.splitButton.clicked.connect(self.create_validation_split)
        self.trainButton = QtWidgets.QPushButton('Train Model', self)
        self.trainButton.clicked.connect(self.train_model)

        for button in [self.loadButton, self.prevButton, self.nextButton, self.acceptButton, self.splitButton, self.trainButton]:
            button.setFixedHeight(40)
            control_layout.addWidget(button)

        main_layout = QtWidgets.QVBoxLayout(self)
        main_layout.addWidget(self.pageLabel)
        main_layout.addLayout(self.gridLayout)
        main_layout.addLayout(control_layout)
        self.setLayout(main_layout)

    def clear_directory(self, directory_path):
        if os.path.exists(directory_path):
            shutil.rmtree(directory_path)
        os.makedirs(directory_path, exist_ok=True)

    def load_images(self):
        # Label the images and prepare the dataset
        self.dataset = self.base_model.label(
            input_folder=IMAGE_DIR_PATH,
            extension=".jpeg",
            output_folder=DATASET_DIR_PATH
        )

        # Set total pages based on the dataset length
        self.total_pages = (len(self.dataset) + 3) // 4
        self.current_page_index = 0  # Start at the first page
        self.annotate_images()

    def annotate_images(self):
        # Calculate the range of images to display on the current page
        start_index = self.current_page_index * 4
        end_index = start_index + 4

        self.pageLabel.setText(f"Page {self.current_page_index + 1} of {self.total_pages}")

        mask_annotator = sv.MaskAnnotator()
        box_annotator = sv.BoxAnnotator()
        label_annotator = sv.LabelAnnotator()

        # Iterate through the dataset and display only the current page's images
        for i, (image_name, annotations) in enumerate(self.dataset):
            if start_index <= i < end_index:
                image_path = os.path.join(IMAGE_DIR_PATH, image_name)
                image = cv2.imread(image_path)

                # Annotate image
                annotated_image = image.copy()
                annotated_image = mask_annotator.annotate(scene=annotated_image, detections=annotations)
                annotated_image = box_annotator.annotate(scene=annotated_image, detections=annotations)
                annotated_image = label_annotator.annotate(scene=annotated_image, detections=annotations)

                # Display image and update status labels
                self.display_image(annotated_image, i - start_index)
                status = self.image_status.get(image_name, "Unreviewed")
                self.statusLabels[i - start_index].setText(f"Status: {status}")
                self.rejectButtons[i - start_index].setChecked(status == "Rejected")

            elif i >= end_index:
                break  # Stop once we've loaded the images for the current page

    def display_image(self, annotated_image, label_index):
        if label_index < len(self.imageLabels):
            qt_image = QtGui.QImage(annotated_image.data, annotated_image.shape[1],
                                    annotated_image.shape[0], QtGui.QImage.Format_RGB888)
            pixmap = QtGui.QPixmap.fromImage(qt_image).scaled(400, 400, QtCore.Qt.KeepAspectRatio)
            self.imageLabels[label_index].setPixmap(pixmap)

    def accept_page(self):
        current_image_names = [
            image_name for i, (image_name, _) in enumerate(self.dataset)
            if self.current_page_index * 4 <= i < (self.current_page_index + 1) * 4
        ]

        for i, image_name in enumerate(current_image_names):
            base_name = os.path.splitext(image_name)[0]
            image_path = os.path.join(TRAIN_IMAGES_DIRECTORY, image_name)
            label_path = os.path.join(TRAIN_LABELS_DIRECTORY, f"{base_name}.txt")

            if self.rejectButtons[i].isChecked():
                self.image_status[image_name] = "Rejected"
                self.statusLabels[i].setText("Status: Rejected")
                if os.path.exists(image_path):
                    os.remove(image_path)
                if os.path.exists(label_path):
                    os.remove(label_path)
            else:
                self.image_status[image_name] = "Accepted"
                self.statusLabels[i].setText("Status: Accepted")
                original_image_path = os.path.join(IMAGE_DIR_PATH, image_name)
                if not os.path.exists(image_path):
                    shutil.copyfile(original_image_path, image_path)

                with open(label_path, "w") as label_file:
                    annotations = self.dataset.detections_map.get(image_name)
                    for class_id, x, y, w, h in annotations.xywh:
                        label_file.write(f"{class_id} {x} {y} {w} {h}\n")

        QtWidgets.QMessageBox.information(self, "Accept", "Processed selected images and labels.")

    def create_validation_split(self):
        train_images = [name for name, _ in self.dataset if self.image_status.get(name) == "Accepted"]
        split_index = int(0.2 * len(train_images))

        for image_name in train_images[:split_index]:
            shutil.move(os.path.join(TRAIN_IMAGES_DIRECTORY, image_name), os.path.join(VALID_IMAGES_DIRECTORY, image_name))

        QtWidgets.QMessageBox.information(self, "Split", "Validation split created from accepted training images.")

    def next_page(self):
        if self.current_page_index < self.total_pages - 1:
            self.current_page_index += 1
            self.clear_grid()
            self.annotate_images()

    def prev_page(self):
        if self.current_page_index > 0:
            self.current_page_index -= 1
            self.clear_grid()
            self.annotate_images()

    def clear_grid(self):
        for label in self.imageLabels:
            label.clear()
        for status_label in self.statusLabels:
            status_label.setText("Unreviewed")

    def train_model(self):
        if not os.listdir(TRAIN_IMAGES_DIRECTORY):
            QtWidgets.QMessageBox.warning(self, "No Images", "No images to train on. Accept some images first.")
            return

        self.target_model = YOLOv8("yolov8s-seg.pt")
        self.target_model.train(DATA_YAML_PATH, epochs=1)
        QtWidgets.QMessageBox.information(self, "Training", "Model training complete.")

if __name__ == '__main__':
    app = QtWidgets.QApplication(sys.argv)
    window = ImageAnnotatorApp()
    window.show()
    sys.exit(app.exec())

###ISSUE##
You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
Labeling C:\Users\xxxxxx\AppData\Local\Temp\tmpdv_uewat\1000000336.jpeg: 0%| | 0/15 [00:00<?, ?it/s]The device argument is deprecated and will be removed in v5 of Transformers.
torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
None of the inputs have requires_grad=True. Gradients will be None
torch.cuda.amp.autocast(args...) is deprecated. Please use torch.amp.autocast('cuda', args...) instead.
Labeling C:\Users\xxxx\AppData\Local\Temp\tmpdv_uewat\1000000350.jpeg: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:28<00:00, 1.91s/it]
Traceback (most recent call last):
File "c:\Users\xxx\xxxxx\version3.py", line 113, in load_images
self.dataset = self.base_model.label(
^^^^^^^^^^^^^^^^^^^^^^
File "c:\Users\xxx\AppData\Local\anaconda3\envs\xxx\Lib\site-packages\supervision\dataset\core.py", line 546, in as_yolo
save_dataset_images(
File "c:\Users\xxxx\AppData\Local\anaconda3\envs\xxx\Lib\site-packages\supervision\dataset\utils.py", line 111, in save_dataset_images
shutil.copyfile(image_path, final_path)
File "c:\Users\xxxxx\AppData\Local\anaconda3\envs\xxx\Lib\shutil.py", line 256, in copyfile
with open(src, 'rb') as fsrc:

[LinasKo]

I believe the important part of the error message is at the end, but got cut off.