The BioMassters

The BioMassters

1st place out of 976 participants with 27.6280 Average RMSE score (top2 27.6779).

Approach

The solution is based on an UNet model with a shared encoder with aggregation via attention. The inputs to the encoder are 15-band images with a resolution of 256x256 from joint Sentinel-1 and Sentinel-2 satellite missions. The encoder is shared for all 12 months. The outputs are aggregated via self-attention. Finally, a decoder takes as inputs the aggregated features and predicts a single yearly agbm. We directly optimize RMSE using AdamW optimizer and CosineAnnelingLR scheduler. We don't compute loss for high agbm values (>400). We use vertical flips, rotations, and random month dropout as augmentations. Month dropout simply removes images.

Highlights

• UNet model
  • Shared tf_efficientnetv2_l_in21k encoder for all months. The input is 15-band images with min-max normalization. Aggregate over all 12 months via self-attention at the encoder level
  • Heavy decoder takes aggregated features and predicts single yearly agbm
• RMSE loss (ignore high values >400)
• Train 900 epochs on one split (fold), then finetune 200 epochs on full dataset, 8 batch size per GPU, mixed precision
  • AdamW optimizer with 1e-3 learning rate and 1e-2 weight decay
  • CosineAnnealingLR scheduler
• Augmentations: random flips, rotations, and month dropout
• Test time augmentations: left-right and up-down flips

Prerequisites & Hardware

• GNU/Linux
• Python 3
• Nvidia Driver Version: 515.65.01
• CUDA Version: 11.7
• PyTorch 1.13
• 2 x GPU Nvidia A100 40GB VRAM
• 4 x CPU AMD Milan 7413 @ 2.65 GHz 128M cache L3
• 64GB RAM
• 8 days for training
• 5 min for inference

Setup

Create an environment using Python 3.8. The solution was originally run on Python 3.8.10. Install the required Python packages

pip install -r requirements.txt

Download the data from the competition page and unzip into data folder.

Training

To run training from the command line

sh ./run.sh

It will take about 8 days on 2 A100 40GB GPUs.

Inference

Download pretrained models and extract into models folder.

unzip models.zip -d models

To run inference from the command line

sh ./submit.sh

It takes ~5 minutes on 1 GPU A100 40GB (note on V100 32GB the results are slightly different).