---
license: cc-by-4.0
---

Runs using the PRUE EfficientNet configs but with training w/o only on CC-BY countries.

Metrics were computed on the FTW Full dataset using `ftw model test -p3 -t3 --temporal_options stacked --model path/to/model.ckpt --countries full_data`

| Model | Pixel Level IoU | Pixel Level Precision | Pixel Level Recall | Object Level Precision | Object Level Recall | Object Level F1 |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| EfficientNet-B3 | 0.76 | 0.87 | 0.86 | 0.54 | 0.31 | 0.39 |
| EfficientNet-B5 | 0.76 | 0.88 | 0.86 | 0.53 | 0.33 | 0.41 |
| EfficientNet-B7 | 0.77 | 0.88 | 0.86 | 0.58 | 0.35 | 0.44 |

Here are the CC-BY-NC checkpoint metrics from the paper for reference:

| Model | Pixel Level IoU | Pixel Level Precision | Pixel Level Recall | Object Level Precision | Object Level Recall | Object Level F1 |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| EfficientNet-B3 | 0.74 | - | - | - | - | 0.43 |
| EfficientNet-B5 | 0.75 | - | - | - | - | 0.46 |
| EfficientNet-B7 | 0.76 | 0.89 | 0.83 | 0.62 | 0.40 | 0.47 |


# Convert the checkpoints for TorchGeo compatibility

```python
import os
import segmentation_models_pytorch as smp
import torch
import hashlib

urls = {
    "efficientnet-b3": "https://github.com/fieldsoftheworld/ftw-baselines/releases/download/v3.1/prue_efnetb3_ccby_checkpoint.ckpt",
    "efficientnet-b5": "https://github.com/fieldsoftheworld/ftw-baselines/releases/download/v3.1/prue_efnetb5_ccby_checkpoint.ckpt",
    "efficientnet-b7": "https://github.com/fieldsoftheworld/ftw-baselines/releases/download/v3.1/prue_efnetb7_ccby_checkpoint.ckpt",
}
for encoder_name, url in urls.items():
    state_dict = torch.hub.load_state_dict_from_url(url, weights_only=True, map_location="cpu")["state_dict"]
    state_dict = {k.replace("model.", ""): v for k, v in state_dict.items()}
    model = smp.Unet(encoder_name=encoder_name, in_channels=8, classes=3, encoder_weights=None)
    model.load_state_dict(state_dict)
    filename = os.path.basename(url).replace("_checkpoint.ckpt", ".pth")
    torch.save(model.state_dict(), filename)
    md5 = hashlib.md5(open(filename, "rb").read()).hexdigest()[:8]
    os.rename(filename, filename.replace(".pth", f"-{md5}.pth"))
```