quoted mean & std value lists

docs/user-guide/advanced/replicate_evaluations.md

@@ -19,8 +19,8 @@ Evaluating the backbone with randomly initialized weights serves as a baseline t
 
 ```
 MODEL_NAME="universal/vit_small_patch16_224_random" \
-NORMALIZE_MEAN=[0.485,0.456,0.406] \
-NORMALIZE_STD=[0.229,0.224,0.225] \
+NORMALIZE_MEAN="[0.485,0.456,0.406]" \
+NORMALIZE_STD="[0.229,0.224,0.225]" \
 IN_FEATURES=384 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -31,8 +31,8 @@ The next baseline model, uses a pretrained ViT-S16 backbone with ImageNet weight
 
 ```
 MODEL_NAME="universal/vit_small_patch16_224_dino" \
-NORMALIZE_MEAN=[0.485,0.456,0.406] \
-NORMALIZE_STD=[0.229,0.224,0.225] \
+NORMALIZE_MEAN="[0.485,0.456,0.406]" \
+NORMALIZE_STD="[0.229,0.224,0.225]" \
 IN_FEATURES=384 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -44,8 +44,8 @@ on [GitHub](https://github.com/lunit-io/benchmark-ssl-pathology/releases/). To e
 
 ```
 MODEL_NAME=pathology/lunit_vits16
-NORMALIZE_MEAN=[0.70322989,0.53606487,0.66096631] \
-NORMALIZE_STD=[0.21716536,0.26081574,0.20723464] \
+NORMALIZE_MEAN="[0.70322989,0.53606487,0.66096631]" \
+NORMALIZE_STD="[0.21716536,0.26081574,0.20723464]" \
 IN_FEATURES=384 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -54,8 +54,8 @@ eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 
 ```
 MODEL_NAME=pathology/lunit_vits8 \
-NORMALIZE_MEAN=[0.70322989,0.53606487,0.66096631] \
-NORMALIZE_STD=[0.21716536,0.26081574,0.20723464] \
+NORMALIZE_MEAN="[0.70322989,0.53606487,0.66096631]" \
+NORMALIZE_STD="[0.21716536,0.26081574,0.20723464]" \
 IN_FEATURES=384 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -67,8 +67,8 @@ eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 
 ```
 MODEL_NAME=pathology/owkin_phikon \
-NORMALIZE_MEAN=[0.485,0.456,0.406] \
-NORMALIZE_STD=[0.229,0.224,0.225] \
+NORMALIZE_MEAN="[0.485,0.456,0.406]" \
+NORMALIZE_STD="[0.229,0.224,0.225]" \
 IN_FEATURES=768 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -80,8 +80,8 @@ be requested.
 
 ```
 MODEL_NAME=pathology/mahmood_uni \
-NORMALIZE_MEAN=[0.485,0.456,0.406] \
-NORMALIZE_STD=[0.229,0.224,0.225] \
+NORMALIZE_MEAN="[0.485,0.456,0.406]" \
+NORMALIZE_STD="[0.229,0.224,0.225]" \
 IN_FEATURES=1024 \
 HF_TOKEN=<your-huggingace-token-for-downloading-the-model> \
 eva predict_fit --config configs/vision/phikon/offline/<task>.yaml
@@ -94,8 +94,8 @@ and available on [GitHub](https://github.com/kaiko-ai/towards_large_pathology_fm
 
 ```
 MODEL_NAME=pathology/kaiko_vits16 \
-NORMALIZE_MEAN=[0.5,0.5,0.5] \
-NORMALIZE_STD=[0.5,0.5,0.5] \
+NORMALIZE_MEAN="[0.5,0.5,0.5]" \
+NORMALIZE_STD="[0.5,0.5,0.5]" \
 IN_FEATURES=384 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -107,8 +107,8 @@ and available on [GitHub](https://github.com/kaiko-ai/towards_large_pathology_fm
 
 ```
 MODEL_NAME=pathology/kaiko_vits8 \
-NORMALIZE_MEAN=[0.5,0.5,0.5] \
-NORMALIZE_STD=[0.5,0.5,0.5] \
+NORMALIZE_MEAN="[0.5,0.5,0.5]" \
+NORMALIZE_STD="[0.5,0.5,0.5]" \
 IN_FEATURES=384 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -120,8 +120,8 @@ and available on [GitHub](https://github.com/kaiko-ai/towards_large_pathology_fm
 
 ```
 MODEL_NAME=pathology/kaiko_vitb16 \
-NORMALIZE_MEAN=[0.5,0.5,0.5] \
-NORMALIZE_STD=[0.5,0.5,0.5] \
+NORMALIZE_MEAN="[0.5,0.5,0.5]" \
+NORMALIZE_STD="[0.5,0.5,0.5]" \
 IN_FEATURES=768 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -133,8 +133,8 @@ and available on [GitHub](https://github.com/kaiko-ai/towards_large_pathology_fm
 
 ```
 MODEL_NAME=pathology/kaiko_vitb16 \
-NORMALIZE_MEAN=[0.5,0.5,0.5] \
-NORMALIZE_STD=[0.5,0.5,0.5] \
+NORMALIZE_MEAN="[0.5,0.5,0.5]" \
+NORMALIZE_STD="[0.5,0.5,0.5]" \
 IN_FEATURES=768 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -146,8 +146,8 @@ and available on [GitHub](https://github.com/kaiko-ai/towards_large_pathology_fm
 
 ```
 MODEL_NAME=pathology/kaiko_vitl14 \
-NORMALIZE_MEAN=[0.5,0.5,0.5] \
-NORMALIZE_STD=[0.5,0.5,0.5] \
+NORMALIZE_MEAN="[0.5,0.5,0.5]" \
+NORMALIZE_STD="[0.5,0.5,0.5]" \
 IN_FEATURES=1024 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -159,8 +159,8 @@ were released on [HuggingFace](https://huggingface.co/bioptimus/H-optimus-0).
 
 ```
 MODEL_NAME=pathology/bioptimus_h_optimus_0 \
-NORMALIZE_MEAN=[0.707223,0.578729,0.703617] \
-NORMALIZE_STD=[0.211883,0.230117,0.177517] \
+NORMALIZE_MEAN="[0.707223,0.578729,0.703617]" \
+NORMALIZE_STD="[0.211883,0.230117,0.177517]" \
 IN_FEATURES=1536 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -171,8 +171,8 @@ To evaluate the [Prov-Gigapath](https://github.com/prov-gigapath/prov-gigapath)
 
 ```
 MODEL_NAME=pathology/prov_gigapath \
-NORMALIZE_MEAN=[0.485,0.456,0.406] \
-NORMALIZE_STD=[0.229,0.224,0.225] \
+NORMALIZE_MEAN="[0.485,0.456,0.406]" \
+NORMALIZE_STD="[0.229,0.224,0.225]" \
 IN_FEATURES=1536 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -185,8 +185,8 @@ a proprietary dataset of one million slides, available for download on
 
 ```
 MODEL_NAME=pathology/histai_hibou_b \
-NORMALIZE_MEAN=[0.7068,0.5755,0.722] \
-NORMALIZE_STD=[0.195,0.2316,0.1816] \
+NORMALIZE_MEAN="[0.7068,0.5755,0.722]" \
+NORMALIZE_STD="[0.195,0.2316,0.1816]" \
 IN_FEATURES=768 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```
@@ -198,8 +198,8 @@ a proprietary dataset of one million slides, available for download on
 
 ```
 MODEL_NAME=pathology/histai_hibou_l \
-NORMALIZE_MEAN=[0.7068,0.5755,0.722] \
-NORMALIZE_STD=[0.195,0.2316,0.1816] \
+NORMALIZE_MEAN="[0.7068,0.5755,0.722]" \
+NORMALIZE_STD="[0.195,0.2316,0.1816]" \
 IN_FEATURES=1024 \
 eva predict_fit --config configs/vision/pathology/offline/<task>.yaml
 ```