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README.md

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+# 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

config.json

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+{
+  "model_type": "prithvi_eo_v2",
+  "backbone": "prithvi_eo_v2_300m",
+  "num_classes": 2,
+  "input_bands": 6,
+  "input_size": [224, 224],
+  "task": "semantic_segmentation",
+  "head": {
+    "type": "upernet",
+    "pool_scales": [1, 2, 3, 6],
+    "fpn_out_channels": 256
+  },
+  "id2label": {
+    "0": "no_flood",
+    "1": "flood"
+  },
+  "label2id": {
+    "no_flood": 0,
+    "flood": 1
+  }
+}

pytorch_model.bin

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