Comments (10)
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 2
Batch count: 512x512
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 1
# Cores: 10
Initial raw usage in seconds: 39644
Final raw usage in seconds: 85184
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 2
Batch count: 512x512
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 2
# Cores: 10
Initial raw usage in seconds: 85184
Final raw usage in seconds: 133344
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 2
Batch count: 1024x1024
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 1
# Cores: 10
Initial raw usage in seconds: 133344
Final raw usage in seconds: 292374
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 2
Batch count: 1024x1024
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 2
# Cores: 10
Initial raw usage in seconds: 292374
Final raw usage in seconds: 452974
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 4
Batch count: 512x512
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 4
# Cores: 10
Initial raw usage in seconds: 455314
Final raw usage in seconds: 537754
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 4
Batch count: 512x512
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 4
# Cores: 10
Initial raw usage in seconds: 650340
Final raw usage in seconds: 733020
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands + 3 GPS)
Batch size: 4
Batch count: 512x512
# epochs: 10
Epoch size: 1000
Cluster: Akya-cuda
# GPU: 4
# Cores: 10
Initial raw usage in seconds: 733020
Final raw usage in seconds: 839800
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 2
Batch count: 512x512
# epochs: 10
Epoch size: 2000
Cluster: Akya-cuda
# GPU: 1
# Cores: 10
Initial raw usage in seconds: 839800
Final raw usage in seconds: 927380
from sentinel_traj_nn.
Model: UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 2
Batch count: 1024x1024
# epochs: 10
Epoch size: 2000
Cluster: Akya-cuda
# GPU: 1
# Cores: 10
Initial raw usage in seconds: 1108100
Final raw usage in seconds: 1431260
from sentinel_traj_nn.
Model: SRCNN+UNET
Dataset: Montreal-small (4 MSI bands)
Batch size: 1
Batch count: 1024x1024
# epochs: 40
Epoch size: 2000
Cluster: Akya-cuda
# GPU: 4
# Cores: 10
Initial raw usage in seconds: 1433434
Final raw usage in seconds: N/A
from sentinel_traj_nn.
Related Issues (20)
- Create custom convolution filter HOT 1
- Add requirements.txt for python libraries
- Resolve filename typo in constract_loss.py HOT 1
- Implement layer wise normalization boundaries.
- Implement ignored images functionality.
- Implement data generator with multiple input sets.
- Run available models on Montreal data
- Run available models on Istanbul data
- Migrate to tensorflow 2.2
- Test new input/output file paths with remote and linux
- Examine the effect of shuffling HOT 1
- Comparison of different loss functions on Montreal and Istanbul with different models
- Add standard deviation for mIoU metric in jupyter notebook
- Create caching mechanism for train, test, validation splits HOT 1
- Run available models on Istanbul+Montreal together
- Create batch experiment running scripts
- Create label coverage % filter
- Run label coverage filtered experiments on all datasets variations
- Create test on different datasets functionality
- Implement swin-unet and transunet
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