diff --git a/configs/core/tests/offline/embeddings.yaml b/configs/core/tests/offline/embeddings.yaml
index f845fe00..5c11cd2a 100644
--- a/configs/core/tests/offline/embeddings.yaml
+++ b/configs/core/tests/offline/embeddings.yaml
@@ -26,11 +26,9 @@ model:
         out_features: &NUM_CLASSES 2
     criterion: torch.nn.CrossEntropyLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: ${oc.env:LR_VALUE, 0.1}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/pathology/offline/classification/bach.yaml b/configs/vision/pathology/offline/classification/bach.yaml
index d4b17ece..bf9494ba 100644
--- a/configs/vision/pathology/offline/classification/bach.yaml
+++ b/configs/vision/pathology/offline/classification/bach.yaml
@@ -53,11 +53,9 @@ model:
         out_features: &NUM_CLASSES 4
     criterion: torch.nn.CrossEntropyLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/pathology/offline/classification/camelyon16.yaml b/configs/vision/pathology/offline/classification/camelyon16.yaml
index 6d898517..5a5f9a5c 100644
--- a/configs/vision/pathology/offline/classification/camelyon16.yaml
+++ b/configs/vision/pathology/offline/classification/camelyon16.yaml
@@ -18,7 +18,7 @@ trainer:
           filename: best
           save_last: true
           save_top_k: 1
-          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
+          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
           mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
diff --git a/configs/vision/pathology/offline/classification/camelyon16_small.yaml b/configs/vision/pathology/offline/classification/camelyon16_small.yaml
index 133350b5..dd4be2af 100644
--- a/configs/vision/pathology/offline/classification/camelyon16_small.yaml
+++ b/configs/vision/pathology/offline/classification/camelyon16_small.yaml
@@ -18,7 +18,7 @@ trainer:
           filename: best
           save_last: true
           save_top_k: 1
-          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
+          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
           mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
diff --git a/configs/vision/pathology/offline/classification/crc.yaml b/configs/vision/pathology/offline/classification/crc.yaml
index bc458a70..feca261e 100644
--- a/configs/vision/pathology/offline/classification/crc.yaml
+++ b/configs/vision/pathology/offline/classification/crc.yaml
@@ -53,11 +53,9 @@ model:
         out_features: &NUM_CLASSES 9
     criterion: torch.nn.CrossEntropyLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
@@ -104,7 +102,7 @@ data:
             split: val
     dataloaders:
       train:
-        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
+        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
         num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
         shuffle: true
       val:
diff --git a/configs/vision/pathology/offline/classification/mhist.yaml b/configs/vision/pathology/offline/classification/mhist.yaml
index 8f423c47..f96c1f15 100644
--- a/configs/vision/pathology/offline/classification/mhist.yaml
+++ b/configs/vision/pathology/offline/classification/mhist.yaml
@@ -18,12 +18,12 @@ trainer:
           filename: best
           save_last: true
           save_top_k: 1
-          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
+          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
           mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
           min_delta: 0
-          patience: 51
+          patience: 70
           monitor: *MONITOR_METRIC
           mode: *MONITOR_METRIC_MODE
       - class_path: eva.callbacks.ClassificationEmbeddingsWriter
@@ -53,11 +53,9 @@ model:
         out_features: 1
     criterion: torch.nn.BCEWithLogitsLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/pathology/offline/classification/patch_camelyon.yaml b/configs/vision/pathology/offline/classification/patch_camelyon.yaml
index 4bce666d..fc8450e7 100644
--- a/configs/vision/pathology/offline/classification/patch_camelyon.yaml
+++ b/configs/vision/pathology/offline/classification/patch_camelyon.yaml
@@ -18,7 +18,7 @@ trainer:
           filename: best
           save_last: true
           save_top_k: 1
-          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
+          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
           mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
@@ -54,11 +54,9 @@ model:
         out_features: 1
     criterion: torch.nn.BCEWithLogitsLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
@@ -118,7 +116,7 @@ data:
             split: test
     dataloaders:
       train:
-        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
+        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
         num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
         shuffle: true
       val:
diff --git a/configs/vision/pathology/online/classification/bach.yaml b/configs/vision/pathology/online/classification/bach.yaml
index 60e8b6ae..1719d821 100644
--- a/configs/vision/pathology/online/classification/bach.yaml
+++ b/configs/vision/pathology/online/classification/bach.yaml
@@ -45,11 +45,9 @@ model:
         out_features: &NUM_CLASSES 4
     criterion: torch.nn.CrossEntropyLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/pathology/online/classification/crc.yaml b/configs/vision/pathology/online/classification/crc.yaml
index cdf743d5..5abe659e 100644
--- a/configs/vision/pathology/online/classification/crc.yaml
+++ b/configs/vision/pathology/online/classification/crc.yaml
@@ -45,11 +45,9 @@ model:
         out_features: &NUM_CLASSES 9
     criterion: torch.nn.CrossEntropyLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
@@ -86,7 +84,7 @@ data:
           split: val
     dataloaders:
       train:
-        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
+        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
         num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
         shuffle: true
       val:
diff --git a/configs/vision/pathology/online/classification/mhist.yaml b/configs/vision/pathology/online/classification/mhist.yaml
index 4c87db98..25dcbc50 100644
--- a/configs/vision/pathology/online/classification/mhist.yaml
+++ b/configs/vision/pathology/online/classification/mhist.yaml
@@ -17,12 +17,12 @@ trainer:
           filename: best
           save_last: true
           save_top_k: 1
-          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
+          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
           mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
           min_delta: 0
-          patience: 51
+          patience: 70
           monitor: *MONITOR_METRIC
           mode: *MONITOR_METRIC_MODE
     logger:
@@ -45,11 +45,9 @@ model:
         out_features: 1
     criterion: torch.nn.BCEWithLogitsLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/pathology/online/classification/patch_camelyon.yaml b/configs/vision/pathology/online/classification/patch_camelyon.yaml
index 95c3bb43..13817a71 100644
--- a/configs/vision/pathology/online/classification/patch_camelyon.yaml
+++ b/configs/vision/pathology/online/classification/patch_camelyon.yaml
@@ -17,7 +17,7 @@ trainer:
           filename: best
           save_last: true
           save_top_k: 1
-          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
+          monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
           mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
@@ -45,11 +45,9 @@ model:
         out_features: 1
     criterion: torch.nn.BCEWithLogitsLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
@@ -91,7 +89,7 @@ data:
           split: test
     dataloaders:
       train:
-        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
+        batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
         num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
         shuffle: true
       val:
diff --git a/configs/vision/tests/offline/panda.yaml b/configs/vision/tests/offline/panda.yaml
index 28844dd1..4051b4ed 100644
--- a/configs/vision/tests/offline/panda.yaml
+++ b/configs/vision/tests/offline/panda.yaml
@@ -34,11 +34,9 @@ model:
         output_size: &NUM_CLASSES 6
     criterion: torch.nn.CrossEntropyLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: &LR_VALUE ${oc.env:LR_VALUE, 0.00004}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/tests/offline/patch_camelyon.yaml b/configs/vision/tests/offline/patch_camelyon.yaml
index 64c503bf..e09a44c6 100644
--- a/configs/vision/tests/offline/patch_camelyon.yaml
+++ b/configs/vision/tests/offline/patch_camelyon.yaml
@@ -47,11 +47,9 @@ model:
         out_features: 1
     criterion: torch.nn.BCEWithLogitsLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: ${oc.env:LR_VALUE, 0.1}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/configs/vision/tests/online/patch_camelyon.yaml b/configs/vision/tests/online/patch_camelyon.yaml
index 14420dc5..4b870941 100644
--- a/configs/vision/tests/online/patch_camelyon.yaml
+++ b/configs/vision/tests/online/patch_camelyon.yaml
@@ -18,11 +18,9 @@ model:
         out_features: 1
     criterion: torch.nn.BCEWithLogitsLoss
     optimizer:
-      class_path: torch.optim.SGD
+      class_path: torch.optim.AdamW
       init_args:
-        lr: ${oc.env:LR_VALUE, 0.1}
-        momentum: 0.9
-        weight_decay: 0.0
+        lr: ${oc.env:LR_VALUE, 0.0003}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.CosineAnnealingLR
       init_args:
diff --git a/docs/images/leaderboard.svg b/docs/images/leaderboard.svg
index 46da272c..2031c979 100644
--- a/docs/images/leaderboard.svg
+++ b/docs/images/leaderboard.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2024-10-11T17:12:02.777458</dc:date>
+    <dc:date>2024-10-18T15:48:36.884888</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
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diff --git a/docs/images/starplot.png b/docs/images/starplot.png
index 97e2cebd..5f600e9c 100644
Binary files a/docs/images/starplot.png and b/docs/images/starplot.png differ
diff --git a/docs/leaderboards.md b/docs/leaderboards.md
index e2aaba2a..66e53f4e 100644
--- a/docs/leaderboards.md
+++ b/docs/leaderboards.md
@@ -27,7 +27,7 @@ For details on the FM-backbones and instructions to replicate the results, check
 
 ## Evaluation protocol
 
-*eva* uses a task- & model-independent and fixed default set up which closely follows the standard evaluation protocol proposed by [1] (with adjustments for slide-level tasks to ensure convergence and computational efficiency).
+*eva* uses a fixed protocol customized to each category of tasks. The setup has proven to be performant and robust independent of task and model size & architecture and generally prioritizes fairness and comparability over state-of-the-art performance.
 
 We selected this approach to prioritize reliable, robust and fair FM-evaluation while being in line with common literature.
 
@@ -38,26 +38,21 @@ We selected this approach to prioritize reliable, robust and fair FM-evaluation
 | **Dropout**                    | 0.0                       | 0.0                       | 0.0                       |
 | **Hidden activation function** | n/a                       | ReLU                      | n/a                       |
 | **Output activation function** | none                      | none                      | none                      |
-| **Number of steps**            | 12,500                    | 12,500 (2)                | 2,000                     |
-| **Base batch size**            | 4,096 (1)                 | 32                        | 64                        |
-| **Base learning rate**         | 0.01 (1)                  | 0.001                     | 0.0001                    |
-| **Early stopping**             | 5% * [Max epochs]         | 10% * [Max epochs] (3)    | 10% * [Max epochs] (3)    |
+| **Number of steps**            | 12,500                    | 12,500 (1)                | 2,000                     |
+| **Base batch size**            | 256                       | 32                        | 64                        |
+| **Base learning rate**         | 0.0003                    | 0.001                     | 0.0001                    |
+| **Early stopping**             | 5% * [Max epochs]         | 10% * [Max epochs] (2)    | 10% * [Max epochs] (2)    |
 | **Optimizer**                  | SGD                       | AdamW                     | AdamW                     |
 | **Momentum**                   | 0.9                       | n/a                       | n/a                       |
 | **Weight Decay**               | 0.0                       | n/a                       | n/a                       |
 | **betas**                      | n/a                       | [0.9, 0.999]              | [0.9, 0.999]              |
 | **LR Schedule**                | Cosine without warmup     | Cosine without warmup     | PolynomialLR              |
 | **Loss**                       | Cross entropy             | Cross entropy             | Dice                      |
-| **number of patches per slide**| 1                         | dataset specific (4)      | dataset specific (4)      |
+| **number of patches per slide**| 1                         | dataset specific (3)      | dataset specific (3)      |
 
 
-(1) For smaller datasets (e.g. BACH with 400 samples) we reduce the batch size to 256 and scale the learning rate accordingly.
+(1) Upper cap at a maximum of 100 epochs.
 
-(2) Upper cap at a maximum of 100 epochs.
+(2) Lower cap at a minimum of 8 epochs.
 
-(3) Lower cap at a minimum of 8 epochs.
-
-(4) Number of patches per slide depends on task and slide size. E.g. for PANDA and Camelyon16 we use a max of 1,000 and 10,000 random patches per slide respectively.
-
-
-- [1]: [Virchow: A Million-Slide Digital Pathology Foundation Model, 2024](https://arxiv.org/pdf/2309.07778.pdf)
+(3) Number of patches per slide depends on task and slide size. E.g. for `PANDASmall` and `Camelyon16Small` we use a max of 200 and 1000 random patches per slide respectively.
diff --git a/tools/data/leaderboard.csv b/tools/data/leaderboard.csv
index c78416df..f30a3a33 100644
--- a/tools/data/leaderboard.csv
+++ b/tools/data/leaderboard.csv
@@ -1,12 +1,12 @@
 bach,crc,mhist,patch_camelyon,camelyon16_small,panda_small,consep,monusac,model
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-0.797,0.95,0.835,0.939,0.834,0.656,0.662,0.554,dino_vitl16_uni
-0.767,0.951,0.836,0.942,0.82,0.645,0.69,0.588,bioptimus_h_optimus_0
-0.758,0.953,0.814,0.948,0.814,0.664,0.661,0.558,prov_gigapath
-0.81,0.934,0.823,0.949,0.832,0.633,0.69,0.586,histai_hibou_l
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-0.867,0.952,0.814,0.921,0.818,0.638,0.703,0.641,dino_vitb8_kaiko
-0.862,0.935,0.822,0.907,0.812,0.65,0.679,0.59,dino_vitl14_kaiko
+0.783,0.94,0.773,0.901,0.767,0.625,0.63,0.537,dino_vits16_lunit
+0.722,0.936,0.799,0.922,0.797,0.64,0.68,0.54,owkin_phikon
+0.797,0.947,0.844,0.936,0.834,0.656,0.662,0.554,dino_vitl16_uni
+0.758,0.958,0.839,0.942,0.82,0.645,0.69,0.588,bioptimus_h_optimus_0
+0.761,0.952,0.829,0.945,0.814,0.664,0.661,0.558,prov_gigapath
+0.816,0.931,0.826,0.951,0.832,0.633,0.69,0.586,histai_hibou_l
+0.802,0.938,0.829,0.904,0.789,0.618,0.611,0.549,dino_vits16_kaiko
+0.829,0.952,0.814,0.885,0.814,0.654,0.688,0.599,dino_vits8_kaiko
+0.835,0.958,0.835,0.907,0.816,0.621,0.636,0.551,dino_vitb16_kaiko
+0.858,0.957,0.823,0.918,0.818,0.638,0.703,0.641,dino_vitb8_kaiko
+0.864,0.936,0.828,0.908,0.812,0.65,0.679,0.59,dino_vitl14_kaiko