Updated docker image to demisto/yolo-coco:1.0.0.115114. PR batch #1/1… (

demisto#37040) * Updated docker image to demisto/yolo-coco:1.0.0.115114. PR batch #1/1 (demisto#37037) Co-authored-by: root <root@1e2de18e0cc3> * release notes --------- Co-authored-by: content-bot <[email protected]> Co-authored-by: root <root@1e2de18e0cc3>
siscale · Nov 5, 2024 · e8720df · e8720df
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diff --git a/Packs/ComputerVisionEngine/Integrations/ComputerVisionEngine/ComputerVisionEngine.yml b/Packs/ComputerVisionEngine/Integrations/ComputerVisionEngine/ComputerVisionEngine.yml
@@ -28,7 +28,7 @@ script:
     - contextPath: ComputerVision
       description: The key holds down the information about detected objects in the picture.
       type: Unknown
-  dockerimage: demisto/yolo-coco:1.0.0.113048
+  dockerimage: demisto/yolo-coco:1.0.0.115114
   runonce: false
   script: '-'
   subtype: python3

diff --git a/Packs/ComputerVisionEngine/ReleaseNotes/1_0_4.md b/Packs/ComputerVisionEngine/ReleaseNotes/1_0_4.md
@@ -0,0 +1,6 @@
+
+#### Integrations
+
+##### Computer Vision Engine
+
+- Updated the Docker image to: *demisto/yolo-coco:1.0.0.115114*.
diff --git a/Packs/ComputerVisionEngine/pack_metadata.json b/Packs/ComputerVisionEngine/pack_metadata.json
@@ -2,7 +2,7 @@
     "name": "ComputerVisionEngine",
     "description": "The ComputerVision Integration by using the Deep-learning library yolo-coco and OpenCV is able to recognize objects on photographs, i.e. planes, luggage, people, dogs, cats, etc.\nThe integration can be used in playbooks to extract objects on rasterized websites in phishing campaigns and making IOCs out of them.\nAdditionally, the integration is useful in CCTV systems significantly reducing the number of false - positives and creating custom workflows in XSOAR playbooks for on-prem physical security!",
     "support": "community",
-    "currentVersion": "1.0.3",
+    "currentVersion": "1.0.4",
     "author": "Maciej Drobniuch",
     "url": "drobniuch.pl",
     "email": "[email protected]",