| ---
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| language: en
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| license: apache-2.0
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| tags:
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| - earth-observation
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| - geospatial
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| - flood-detection
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| - prithvi
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| - pytorch
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| - semantic-segmentation
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| - sentinel-2
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| datasets:
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| - sen1floods11
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| metrics:
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| - iou
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| - accuracy
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| - f1
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| model-index:
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| - name: Prithvi-2.0 300M - Flood Detection (Sen1Floods11)
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| results:
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| - task:
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| type: semantic-segmentation
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| name: Semantic Segmentation
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| dataset:
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| name: Sen1Floods11
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| type: sen1floods11
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| split: test
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| metrics:
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| - type: iou
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| value: 0.7196
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| name: Flood IoU
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| - type: f1
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| value: 0.8370
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| name: Flood F1
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| - type: accuracy
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| value: 0.9633
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| name: Overall Accuracy
|
| ---
|
|
|
| # Prithvi-2.0 300M - Fine-tuned for Flood Detection (Sen1Floods11)
|
|
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| 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.
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|
|
| ## Model Description
|
|
|
| - **Developed by:** Tushar Thokdar
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| - **Model Type:** Semantic Segmentation
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| - **Backbone:** Prithvi-2.0 300M (ViT-Base)
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| - **Segmentation Head:** UPerNet
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| - **Input Resolution:** 224x224
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| - **Input Bands:** 6 (Red, Green, Blue, Narrow NIR, SWIR 1, SWIR 2)
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| - **Fine-tuned on:** Sen1Floods11 dataset
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|
|
| ## Performance Metrics (Official Test Split)
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|
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| Metrics derived from fine-tuning for 80 epochs on the official Sen1Floods11 test split.
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|
|
| ### Model Metrics
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|
|
| | Metric | Fine-Tuned (80 Epochs) | Baseline | Gain (%) |
|
| | -------------------- | ---------------------- | -------- | -------- |
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| | **Flood IoU** | 0.7196 | 0.1339 | +437.3% |
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| | **Flood F1** | 0.8370 | 0.2362 | +254.3% |
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| | **Flood Precision** | 0.8902 | 0.1638 | +443.4% |
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| | **Flood Recall** | 0.7897 | 0.4234 | +86.5% |
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| | **Mean IoU** | 0.8396 | 0.3953 | +112.3% |
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| | **Overall Accuracy** | 0.9633 | 0.6741 | +42.9% |
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|
|
| ### Per-Class Metrics (Fine-Tuned)
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|
|
| - **No Flood IoU:** 0.9595
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| - **No Flood F1:** 0.9793
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| - **Flood IoU:** 0.7196
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| - **Flood F1:** 0.8370
|
|
|
| ## Training Configuration
|
|
|
| - **Epochs:** 80
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| - **Batch Size:** 16
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| - **Learning Rate:** 5e-5
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| - **Loss:** Dice (0.5) + Focal (0.5)
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| - **Data Splits:** Official (252 Train / 89 Val / 90 Test)
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|
|
| ## Inference Performance
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|
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| - **Throughput:** 20.66 samples/sec (NVIDIA T4)
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| - **Inference Time (Avg):** 0.048s per sample
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|
|
| ## Usage Instructions
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|
|
| To use this model with the `godel-train` library:
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|
|
| ```python
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| import torch
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| from godel_train.models.factory import ModelFactory
|
|
|
| # 1. Initialize model with appropriate config
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| model = ModelFactory.segmentation(
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| backbone="prithvi_eo_v2_300m",
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| num_classes=2,
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| checkpoint_path="pytorch_model.bin" # Local or downloaded file
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| )
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| model.eval()
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|
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| # 2. Prepare sample input (Batch, 6 Bands, 224, 224)
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| # Bands: Red, Green, Blue, Narrow NIR, SWIR 1, SWIR 2
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| sample_input = torch.randn(1, 6, 224, 224)
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|
|
| # 3. Run Inference
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| with torch.no_grad():
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| prediction = model(sample_input)
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| # prediction shape: [1, 2, 224, 224] (Logits)
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|
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| mask = torch.argmax(prediction, dim=1)
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| # mask shape: [1, 224, 224] (0: No Flood, 1: Flood)
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| ```
|
|
|
| ## Data and Credits
|
|
|
| - **Dataset:** Sen1Floods11
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| - **Fine-tuning:** Performed by Tushar Thokdar
|
| - **Foundation Model:** IBM/NASA Prithvi-2.0
|
|
|
