CSIRO Pasture Biomass Prediction (4th Place Solution)

Model Description

• Backbone: vit_huge_plus_patch16_dinov3.lvd1689m (DINOv3)
• Input: RGB pasture image + tabular features (Pre_GSHH_NDVI, Height_Ave_cm)
• Output: Biomass regression targets
• Loss: Weighted Smooth L1 with weights [0.1, 0.1, 0.1, 0.2, 0.5]

• Input: RGB pasture image only
• Output: Predicts tabular features (NDVI, Height)
• Purpose: Feature enrichment for the main model

Model Weights

• Main model: https://huggingface.co/jatinmehra/CSIRO-DinoV3-HugePlus-LB76
• Aux model: https://huggingface.co/jatinmehra/CSIRO-AUX_MODEL

Intended Use

• Research and benchmarking for pasture biomass regression from aerial/ground images.
• Demonstration of multi-modal fusion and auxiliary prediction benefits.

Out-of-Scope Use

• Medical or safety-critical applications.
• Real-time decision systems without domain validation.

Training Data

• Kaggle CSIRO Pasture Biomass Prediction dataset.
• Images were manually cropped to remove cardboard background.

Preprocessing

• Manual cropping to remove cardboard borders.
• Tabular features and regression targets normalized via StandardScaler.
• Image size: 800×800.

Training Configuration

• Batch size: 10
• Optimizer: AdamW
• LR: 5e-5
• Scheduler: CosineAnnealingWarmRestarts (T_0=10, T_mult=2, eta_min=1e-6)
• 5-fold CV (Seed 42)

• Batch size: 8
• Optimizer: AdamW
• LR: 5e-5
• Scheduler: ReduceLROnPlateau (factor=0.5, patience=4)
• 5-fold CV (Seed 44)

Results

Limitations

• Trained on competition data with specific capture setup; generalization to other pasture imagery may vary.
• Large ViT-Huge backbone is compute-intensive.

Ethical Considerations

• Dataset-specific biases may affect generalization.
• Manual preprocessing may not be reproducible without the same data access.

Citation

License