Getting Started
Coming Soon.
Example:
local_path_to_dataset_root = '/Users/user/segmentation_datasets/Segmentation_Data'
imgs_remote_location = 'https://storage.googleapis.com/galileo-public-data/CV_datasets/Segmentation_Data'
transforms = transforms.Compose([transfroms.Resize((512, 512)
train_dataset = ADE20k(transforms=transforms, train=True)
val_dataset = ADE20k(transforms=transforms, train=False)
train_dataloader = torch.utils.DataLoader(train_dataset)
val_dataloader = torch.utils.DataLoader(val_dataset)
# background label is the 0th logit, plane is the 1st, etc.
labels = ["Background", "Plane", "Ship"]
dq.set_labels_for_run(labels)
model = UNet()
watch(
model,
imgs_remote_location=imgs_remote_location
local_path_to_dataset_root=local_path_to_dataset_root,
dataloaders={"training": train_dataloader,
"validation": val_dataloader},
)
# train your model
for epoch in range(epochs):
# train
dq.finish()
Important Note: Dataloaders provided should have no cropping transforms applied to images, only resizing and color augmentations are allowed. Dataloaders provided do not have to be the same as used in training as we recognized cropping can be integral to training, if you use cropping during training please provide separate dataloaders here that do not use cropping.
Last modified 2mo ago