Recent Releases of cultionet
cultionet - 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
- Cultionet uses the UNet 3+ style of deep supervision along three decoders
- Improved training efficiency using PyTorch’s parallel data loader
- Improved inference efficiency using PyTorch’s batch loader
Natural Resources - Soil and Land
- Python
Published by jgrss about 2 years ago
cultionet - 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_workersoption inDataLoaderfor faster train/predict - Added .pt data compression by changing
torch.save|loadtojoblib.dump|load
Natural Resources - Soil and Land
- Python
Published by jgrss over 2 years ago