File size: 1,564 Bytes
9d376ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | # 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"
)
```
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