v1.7.3

• Addressed Issue #63 (#63)
• Better version dependency pinning
• Improved handling of image resolution and height/width dimensions

v1.7.2

What changed?

• Upgraded geowombat (#72)

v1.7.1

What changed?

• Pinned the upper version of pytorch-lightning and upgraded geowombat (#71)

v1.7.0

What changed?

• Improved and fixed issues in the ResUNet 3+ Psi architecture, which was introduced in v1.6.5
• More flexible user arguments. The user can now specify:
  • the model architecture
  • convolution blocks
  • dilations
  • attention weights
• Improvements in the train optimizer stability
• Deep supervision
  • Cultionet uses the UNet 3+ style of deep supervision along three decoders
    • These are optional during training
• Improved training efficiency using PyTorch’s parallel data loader
• Improved inference efficiency using PyTorch’s batch loader

v1.6.5

What changed?

We upgraded geowombat to v2.1.4 following a bug fix.

v1.6.4

v1.6.4

v1.6.3

v1.6.3

v1.6.2

What changed?

setuptools was bumped from >=59.5.0 to >=65.5.1

v1.6.1

v1.6.1

v1.6.0

What's new?

• New architecture design based on UNet 3+ and residual convolutions
  • The new design is a multi-head connection of the UNet 3+ architecture
  • Added optional crop-type model for finer crop learning
• Modified total loss quantification with deep supervision of crop type in RNN layer
  • The tanimoto loss is used on all layers
• Added num_workers option in DataLoader for faster train/predict
• Added .pt data compression by changing torch.save|load to joblib.dump|load