---
language: en
license: apache-2.0
tags:
  - earth-observation
  - geospatial
  - flood-detection
  - prithvi
  - pytorch
  - semantic-segmentation
  - sentinel-2
datasets:
  - sen1floods11
metrics:
  - iou
  - accuracy
  - f1
model-index:
  - name: Prithvi-2.0 300M - Flood Detection (Sen1Floods11)
    results:
      - task:
          type: semantic-segmentation
          name: Semantic Segmentation
        dataset:
          name: Sen1Floods11
          type: sen1floods11
          split: test
        metrics:
          - type: iou
            value: 0.7196
            name: Flood IoU
          - type: f1
            value: 0.8370
            name: Flood F1
          - type: accuracy
            value: 0.9633
            name: Overall Accuracy
---

# Prithvi-2.0 300M - Fine-tuned for Flood Detection (Sen1Floods11)

This model is a fine-tuned version of the **Prithvi-2.0 300M** foundation model, specialized for binary flood detection using Sentinel-2 optical imagery.

## Model Description

- **Developed by:** Tushar Thokdar
- **Model Type:** Semantic Segmentation
- **Backbone:** Prithvi-2.0 300M (ViT-Base)
- **Segmentation Head:** UPerNet
- **Input Resolution:** 224x224
- **Input Bands:** 6 (Red, Green, Blue, Narrow NIR, SWIR 1, SWIR 2)
- **Fine-tuned on:** Sen1Floods11 dataset

## Performance Metrics (Official Test Split)

Metrics derived from fine-tuning for 80 epochs on the official Sen1Floods11 test split.

### Model Metrics

| Metric               | Fine-Tuned (80 Epochs) | Baseline | Gain (%) |
| -------------------- | ---------------------- | -------- | -------- |
| **Flood IoU**        | 0.7196                 | 0.1339   | +437.3%  |
| **Flood F1**         | 0.8370                 | 0.2362   | +254.3%  |
| **Flood Precision**  | 0.8902                 | 0.1638   | +443.4%  |
| **Flood Recall**     | 0.7897                 | 0.4234   | +86.5%   |
| **Mean IoU**         | 0.8396                 | 0.3953   | +112.3%  |
| **Overall Accuracy** | 0.9633                 | 0.6741   | +42.9%   |

### Per-Class Metrics (Fine-Tuned)

- **No Flood IoU:** 0.9595
- **No Flood F1:** 0.9793
- **Flood IoU:** 0.7196
- **Flood F1:** 0.8370

## Training Configuration

- **Epochs:** 80
- **Batch Size:** 16
- **Learning Rate:** 5e-5
- **Loss:** Dice (0.5) + Focal (0.5)
- **Data Splits:** Official (252 Train / 89 Val / 90 Test)

## Inference Performance

- **Throughput:** 20.66 samples/sec (NVIDIA T4)
- **Inference Time (Avg):** 0.048s per sample

## Usage Instructions

To use this model with the `godel-train` library:

```python
import torch
from godel_train.models.factory import ModelFactory

# 1. Initialize model with appropriate config
model = ModelFactory.segmentation(
    backbone="prithvi_eo_v2_300m",
    num_classes=2,
    checkpoint_path="pytorch_model.bin" # Local or downloaded file
)
model.eval()

# 2. Prepare sample input (Batch, 6 Bands, 224, 224)
# Bands: Red, Green, Blue, Narrow NIR, SWIR 1, SWIR 2
sample_input = torch.randn(1, 6, 224, 224)

# 3. Run Inference
with torch.no_grad():
    prediction = model(sample_input)
    # prediction shape: [1, 2, 224, 224] (Logits)

    mask = torch.argmax(prediction, dim=1)
    # mask shape: [1, 224, 224] (0: No Flood, 1: Flood)
```

## Data and Credits

- **Dataset:** Sen1Floods11
- **Fine-tuning:** Performed by Tushar Thokdar
- **Foundation Model:** IBM/NASA Prithvi-2.0