Chris-hughes10

from torch.utils.data import Dataset

class EfficientDetDataset(Dataset):
    def __init__(
        self, dataset_adaptor, transforms=get_valid_transforms()
    ):
        self.ds = dataset_adaptor
        self.transforms = transforms

    def __getitem__(self, index):

Chris-hughes10

import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2


def get_train_transforms(target_img_size=512):
    return A.Compose(
        [
            A.HorizontalFlip(p=0.5),
            A.Resize(height=target_img_size, width=target_img_size, p=1),
            A.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),

Chris-hughes10

from effdet.config.model_config import efficientdet_model_param_dict
from effdet import get_efficientdet_config, EfficientDet, DetBenchTrain
from effdet.efficientdet import HeadNet
from effdet.config.model_config import efficientdet_model_param_dict

def create_model(num_classes=1, image_size=512, architecture="tf_efficientnetv2_l"):
    efficientdet_model_param_dict['tf_efficientnetv2_l'] = dict(
        name='tf_efficientnetv2_l',
        backbone_name='tf_efficientnetv2_l',
        backbone_args=dict(drop_path_rate=0.2),

Chris-hughes10

from pathlib import Path

import PIL

import numpy as np

class CarsDatasetAdaptor:
    def __init__(self, images_dir_path, annotations_dataframe):
        self.images_dir_path = Path(images_dir_path)
        self.annotations_df = annotations_dataframe