pin anomalib version to v2.0.0

.gitattributes

@@ -1 +0,0 @@
-*.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -5,7 +5,6 @@ reports/
 *.code-workspace
 # credentials
 CREDENTIALS*
-*.pth
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

pyproject.toml

@@ -1,11 +1,11 @@
 [project]
 name = "dopt-sensor-anomalies"
-version = "0.1.5"
+version = "0.1.3"
 description = "anomaly detection for sensor images for quality assurance processes"
 authors = [
     {name = "d-opt GmbH (resp.: Florian Foerster)", email = "f.foerster@d-opt.com"},
 ]
-dependencies = ["imutils>=0.5.4", "dopt-basics>=0.2.4", "numpy>=2.2.6", "open-clip-torch>=3.2.0", "anomalib==2.1.0", "dotenv>=0.9.9"]
+dependencies = ["imutils>=0.5.4", "dopt-basics>=0.2.4", "numpy>=2.2.6", "open-clip-torch>=3.2.0", "anomalib==2.0.0"]
 requires-python = "<3.14,>=3.11"
 readme = "README.md"
 license = {text = "LicenseRef-Proprietary"}
@@ -77,7 +77,7 @@ directory = "reports/coverage"
 
 
 [tool.bumpversion]
-current_version = "0.1.5"
+current_version = "0.1.3"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

src/dopt_sensor_anomalies/constants.py

@@ -6,10 +6,9 @@ STOP_FOLDER_NAME: Final[str] = "python"
 MODEL_FOLDER_NAME: Final[str] = "models"
 
 THRESHOLD_BW: Final[int] = 63
-BACKBONE: Final[str] = "wide_resnet50_2"
-LAYERS: Final[tuple[str, ...]] = ("layer1", "layer2", "layer3")
-RATIO: Final[float] = 0.01
-ANOMALY_THRESHOLD: Final[float] = 0.14
+BACKBONE: Final[str] = "resnet18"
+LAYERS: Final[tuple[str, str]] = ("layer1", "layer2")
+RATIO: Final[float] = 0.05
 
 NUM_VALID_ELECTRODES: Final[int] = 6
 HEATMAP_FILENAME_SUFFIX: Final[str] = "_Heatmap"

src/dopt_sensor_anomalies/detection.py

@@ -176,7 +176,8 @@ def infer_image(
         output = model(input_tensor)
 
     anomaly_score = output.pred_score.item()
-    anomaly_label = bool(1 if anomaly_score >= const.ANOMALY_THRESHOLD else 0)
+    print(f">>> Anomaly score: {anomaly_score}")
+    anomaly_label = bool(1 if anomaly_score >= 0.2 else 0)
     anomaly_map = output.anomaly_map.squeeze().cpu().numpy()
 
     img_np = np.array(pil_image)

tests/_models/README.md

@@ -1,3 +0,0 @@
-**PLACE MODELS IN THIS FOLDER**
-
-The model files are too large. Download the files from our internal distribution channel (primarily cloud).