Fix SuperSimpleNet pretrained weights. (#2712)

* Add pretrained weights fix

Signed-off-by: blaz-r <blaz.rolih@gmail.com>

* Update SSN readme

Co-authored-by: Samet Akcay <samet.akcay@intel.com>
Signed-off-by: Blaž Rolih <61357777+blaz-r@users.noreply.github.com>

* Fix readme linting error

Signed-off-by: blaz-r <blaz.rolih@gmail.com>

---------

Signed-off-by: blaz-r <blaz.rolih@gmail.com>
Signed-off-by: Blaž Rolih <61357777+blaz-r@users.noreply.github.com>
Co-authored-by: Samet Akcay <samet.akcay@intel.com>

Author: blaz-r

src/anomalib/models/image/supersimplenet/README.md

@@ -28,6 +28,12 @@ This implementation supports both unsupervised and supervised setting, but Anoma
 
 `anomalib train --model SuperSimpleNet --data MVTecAD --data.category <category>`
 
+> IMPORTANT!
+>
+> The model is verified to work with WideResNet50 using torchvision V1 weights.
+> It should work with most ResNets and WideResNets, but make sure you use V1 weights if you use default noise std value.
+> Correct weight name ends with ".tv\_[...]", not "tv2" (e.g. "wide_resnet50_2.tv_in1k").
+>
 > It is recommended to train the model for 300 epochs with batch size of 32 to achieve stable training with random anomaly generation. Training with lower parameter values will still work, but might not yield the optimal results.
 >
 > For supervised learning, refer to the [official code](https://github.com/blaz-r/SuperSimpleNet).

src/anomalib/models/image/supersimplenet/lightning_model.py

@@ -61,7 +61,7 @@ class Supersimplenet(AnomalibModule):
 
     Args:
         perlin_threshold (float): threshold value for Perlin noise thresholding during anomaly generation.
-        backbone (str): backbone name
+        backbone (str): backbone name. IMPORTANT! use only backbones with torchvision V1 weights ending on ".tv".
         layers (list[str]): backbone layers utilised
         supervised (bool): whether the model will be trained in supervised mode. False by default (unsupervised).
         pre_processor (PreProcessor | bool, optional): Pre-processor instance or
@@ -77,7 +77,7 @@ class Supersimplenet(AnomalibModule):
     def __init__(
         self,
         perlin_threshold: float = 0.2,
-        backbone: str = "wide_resnet50_2",
+        backbone: str = "wide_resnet50_2.tv_in1k",  # IMPORTANT: use .tv weights, not tv2
         layers: list[str] = ["layer2", "layer3"],  # noqa: B006
         supervised: bool = False,
         pre_processor: PreProcessor | bool = True,

src/anomalib/models/image/supersimplenet/torch_model.py

@@ -33,15 +33,15 @@ class SupersimplenetModel(nn.Module):
 
     Args:
         perlin_threshold (float): threshold value for Perlin noise thresholding during anomaly generation.
-        backbone (str): backbone name
+        backbone (str): backbone name. IMPORTANT! use only backbones with torchvision V1 weights ending on ".tv".
         layers (list[str]): backbone layers utilised
         stop_grad (bool): whether to stop gradient from class. to seg. head.
     """
 
     def __init__(
         self,
         perlin_threshold: float = 0.2,
-        backbone: str = "wide_resnet50_2",
+        backbone: str = "wide_resnet50_2.tv_in1k",  # IMPORTANT: use .tv weights, not tv2
         layers: list[str] = ["layer2", "layer3"],  # noqa: B006
         stop_grad: bool = True,
     ) -> None: