Update README.md

README.md

@@ -1,3 +1,34 @@
----
-license: mit
----
+---
+license: mit
+---
+
+Rehosted from https://huggingface.co/saadlahrichi/WSTSPlus
+
+Converted using the following code:
+
+```python
+import os
+import segmentation_models_pytorch as smp
+import torch
+import hashlib
+
+urls = {
+    "t1-all": "https://hf.co/saadlahrichi/WSTSPlus/resolve/main/trained_model_weights/Res18Unet_T1/All/fold6_testAP0.577.pth",
+    "t1-multi": "https://hf.co/saadlahrichi/WSTSPlus/resolve/main/trained_model_weights/Res18Unet_T1/Multi/fold6_testAP0.585.pth",
+    "t1-veg": "https://hf.co/saadlahrichi/WSTSPlus/resolve/main/trained_model_weights/Res18Unet_T1/Veg/fold2_testAP0.567.pth",
+    "t5-all": "https://hf.co/saadlahrichi/WSTSPlus/resolve/main/trained_model_weights/Res18Unet_T5/All/fold2_testAP0.594.pth",
+    "t5-multi": "https://hf.co/saadlahrichi/WSTSPlus/resolve/main/trained_model_weights/Res18Unet_T5/Multi/fold2_testAP0.597.pth",
+    "t5-veg": "https://hf.co/saadlahrichi/WSTSPlus/resolve/main/trained_model_weights/Res18Unet_T5/Veg/fold2_testAP0.590.pth",
+}
+for prefix, url in urls.items():
+    state_dict = torch.hub.load_state_dict_from_url(url, weights_only=True, map_location="cpu")
+    state_dict = {k.replace("model.", ""): v for k, v in state_dict.items()}
+    in_channels = state_dict["encoder.conv1.weight"].shape[1]
+    print(f"{prefix}: in_channels={in_channels}")
+    model = smp.Unet(encoder_name="resnet18", in_channels=in_channels, classes=1, encoder_weights=None)
+    model.load_state_dict(state_dict, strict=True)
+    filename = f"unet-resnet18-{prefix}.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"))
+```