Hi,
I’m struggling to adapt the official gluoncv YoloV3 to a real-life dataset
My data is annotated with SageMaker groundtruth, and I created a custom Dataset
class that returns tuples of {images, annotations} and works fine to train the gluoncv SSD model
When I use this Dataset
in the YoloV3 training script, I have this error:
AssertionError: The number of attributes in each data sample should contains 7 elements, given 2.
in practice, I use this code for the data pipeline (copied from the gluoncv website):
from gluoncv.data.transforms.presets.yolo import YOLO3DefaultTrainTransform
width, height = 416, 416 # resize image to 416x416 after all data augmentation
train_transform = YOLO3DefaultTrainTransform(width, height)
from gluoncv.data.batchify import Tuple, Stack, Pad
batchify_fn = Tuple(*([Stack() for _ in range(6)] + [Pad(axis=0, pad_val=-1) for _ in range(1)]))
train_data = gluon.data.DataLoader(
dataset=train_dataset.transform(train_transform),
batch_size=16,
shuffle=True,
batchify_fn=batchify_fn,
last_batch='rollover',
num_workers=1)
and both a batches = [B for B in train_data]
and the training loop return the above-mentioned error
Is there a required Dataset format for gluoncv Yolov3 training? Where is this documented?