Use correct deepfake checkpoint and reject incompatible weights

main.py

@@ -247,9 +247,34 @@ def load_checkpoint_model():
         try:
             checkpoint = torch.load(path, map_location=DEVICE, weights_only=False)
             state_dict = checkpoint.get("model_state_dict", checkpoint)
-            model.load_state_dict(state_dict, strict=False)
+
+            # Only keep keys that belong to this architecture and match tensor shapes.
+            model_state = model.state_dict()
+            filtered_state = {}
+            for key, value in state_dict.items():
+                if key in model_state and hasattr(value, "shape") and model_state[key].shape == value.shape:
+                    filtered_state[key] = value
+
+            if not filtered_state:
+                print(f"Rejected checkpoint (no compatible keys): {path}")
+                continue
+
+            load_result = model.load_state_dict(filtered_state, strict=False)
+
+            # Guardrail: require substantial overlap so unrelated checkpoints don't load.
+            loaded_ratio = len(filtered_state) / max(len(model_state), 1)
+            if loaded_ratio < 0.7:
+                print(
+                    f"Rejected checkpoint (too few compatible keys: {len(filtered_state)}/{len(model_state)} = {loaded_ratio:.2%}): {path}"
+                )
+                continue
+
             loaded_any = True
             print(f"Loaded checkpoint: {path}")
+            print(
+                f"Compatible keys: {len(filtered_state)}/{len(model_state)} | "
+                f"Missing: {len(load_result.missing_keys)} | Unexpected ignored: {len(load_result.unexpected_keys)}"
+            )
             break
         except Exception as ex:
             print(f"Failed loading checkpoint {path}: {ex}")

models/best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b302642edfb3dfc5986fae645ba3538a97ecb1ac5e2b3218ce3bfc8e30cef9b4
+size 19494087