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README.md

@@ -1,3 +1,60 @@
----
-license: mit
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+# Spatial - Sat2Map Model
+
+Satellite-to-map prediction model trained on OlmoEarth data using the PlanB/nanochat framework.
+
+## WandB Run
+
+[Training Run](https://wandb.ai/viharikvs-urbankisaan/nanochat-sat2map/runs/z9aeknl6?nw=nwuserviharikvs)
+
+## Training Progress
+
+| Step | Val Loss |
+|------|----------|
+| 1000 | 0.3207 |
+| 1500 | 0.4950 |
+| 2000 | 1.0681 |
+
+## 8-GPU Evaluation Results (DDP Aggregated)
+
+Evaluation using all 8 GPUs with `--eval-batches 200` and `--batch-size 2` (400 examples per split), aggregating totals across ranks.
+
+### Step 1000 (Recommended)
+
+| Metric | Value |
+|--------|-------|
+| Val Loss | 0.3668 |
+| Val Accuracy | 0.8813 |
+| WorldCover Accuracy | 0.8892 |
+| CDL Accuracy | 0.8483 |
+
+### Step 1500
+
+| Metric | Value |
+|--------|-------|
+| Val Loss | 0.5635 |
+| Val Accuracy | 0.8680 |
+| WorldCover Accuracy | 0.8755 |
+| CDL Accuracy | 0.8364 |
+
+**Conclusion:** Use step 1000 checkpoint (better val loss + accuracy). Step 1500 is fitting train harder but generalizing worse.
+
+## Repository Contents
+
+- `sat2map_checkpoints/d20_sat2map/` - Model checkpoints (steps 500, 1000, 1500, 2000)
+- `sat2map_dataset/sat2map_g16_t12_target64_k1024/` - Training and test dataset
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download best checkpoint (step 1000)
+model_path = hf_hub_download(
+    repo_id="Viharikvs/spatial",
+    filename="sat2map_checkpoints/d20_sat2map/model_001000.pt"
+)
+meta_path = hf_hub_download(
+    repo_id="Viharikvs/spatial",
+    filename="sat2map_checkpoints/d20_sat2map/meta_001000.json"
+)
+```