diff --git a/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml b/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml
index 11d5393..62bc883 100644
--- a/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml
+++ b/gluefactory/configs/superpoint+lsd+gluestick-homography.yaml
@@ -1,14 +1,14 @@
 data:
     name: homographies
     homography:
-        difficulty: 0.5
-        max_angle: 30
+        difficulty: 0.7
+        max_angle: 45
         patch_shape: [640, 480]
     photometric:
         p: 0.75
     train_size: 900000
     val_size: 1000
-    batch_size: 80  # 20 per 10GB of GPU mem (12 for triplet)
+    batch_size: 160  # 20 per 10GB of GPU mem (12 for triplet)
     num_workers: 15
 model:
     name: gluefactory.models.two_view_pipeline
@@ -70,4 +70,4 @@ train:
         n_steps: 4
     submodules: []
     # clip_grad: 10  # Use only with mixed precision
-    # load_experiment: 
\ No newline at end of file
+    # load_experiment:
\ No newline at end of file
diff --git a/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml b/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml
index 14ff90a..5946826 100644
--- a/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml
+++ b/gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml
@@ -1,10 +1,15 @@
 data:
     name: gluefactory.datasets.megadepth
+    train_num_per_scene: 300
+    val_pairs: valid_pairs.txt
     views: 2
+    min_overlap: 0.1
+    max_overlap: 0.7
+    num_overlap_bins: 3
     preprocessing:
         resize: 640
         square_pad: True
-    batch_size: 60
+    batch_size: 160
     num_workers: 15
 model:
     name: gluefactory.models.two_view_pipeline
@@ -53,9 +58,9 @@ model:
 train:
     seed: 0
     epochs: 200
-    log_every_iter: 10
-    eval_every_iter: 100
-    save_every_iter: 500
+    log_every_iter: 400
+    eval_every_iter: 700
+    save_every_iter: 1400
     lr: 1e-4
     lr_schedule:
         type: exp  # exp or multi_step
diff --git a/gluefactory/datasets/eth3d.py b/gluefactory/datasets/eth3d.py
index ca5e264..44fd73f 100644
--- a/gluefactory/datasets/eth3d.py
+++ b/gluefactory/datasets/eth3d.py
@@ -194,7 +194,7 @@ class ETH3DDataset(BaseDataset):
         if tmp_dir.exists():
             shutil.rmtree(tmp_dir)
         tmp_dir.mkdir(exist_ok=True, parents=True)
-        url_base = "https://cvg-data.inf.ethz.ch/ETH3D_undistorted/"
+        url_base = "https://cvg-data.inf.ethz.ch/SOLD2/SOLD2_ETH3D_undistorted/"
         zip_name = "ETH3D_undistorted.zip"
         zip_path = tmp_dir / zip_name
         torch.hub.download_url_to_file(url_base + zip_name, zip_path)
diff --git a/gluefactory/models/lines/deeplsd.py b/gluefactory/models/lines/deeplsd.py
index 122f4b4..d1aa57d 100644
--- a/gluefactory/models/lines/deeplsd.py
+++ b/gluefactory/models/lines/deeplsd.py
@@ -41,7 +41,7 @@ class DeepLSD(BaseModel):
 
         if not path.parent.is_dir():
             path.parent.mkdir(parents=True, exist_ok=True)
-        link = "https://www.polybox.ethz.ch/index.php/s/XVb30sUyuJttFys/download"
+        link = "https://cvg-data.inf.ethz.ch/DeepLSD/deeplsd_md.tar"
         cmd = ["wget", link, "-O", path]
         print("Downloading DeepLSD model...")
         subprocess.run(cmd, check=True)
diff --git a/gluefactory/models/matchers/gluestick.py b/gluefactory/models/matchers/gluestick.py
index e16a8a5..b46af13 100644
--- a/gluefactory/models/matchers/gluestick.py
+++ b/gluefactory/models/matchers/gluestick.py
@@ -131,7 +131,7 @@ class GlueStick(BaseModel):
                 state_dict = {
                     k.replace("module.", ""): v for k, v in state_dict.items()
                 }
-            self.load_state_dict(state_dict)
+            self.load_state_dict(state_dict, strict=False)
 
     def _forward(self, data):
         device = data["keypoints0"].device
@@ -200,8 +200,6 @@ class GlueStick(BaseModel):
         kpts0 = normalize_keypoints(kpts0, image_size0)
         kpts1 = normalize_keypoints(kpts1, image_size1)
 
-        assert torch.all(kpts0 >= -1) and torch.all(kpts0 <= 1)
-        assert torch.all(kpts1 >= -1) and torch.all(kpts1 <= 1)
         desc0 = desc0 + self.kenc(kpts0, data["keypoint_scores0"])
         desc1 = desc1 + self.kenc(kpts1, data["keypoint_scores1"])