@@ -3719,7 +3702,7 @@ NOTE: You can use RGB images in JPG format (Images folder) and the groundtruth a
NOTE: You can find an example of the organization of the folders at: https://s3.eu-west-2.amazonaws.com/activeeon-public/datasets/oxford.zip
. _Object Detection Dataset:_ Two folders are demanded: the first folder should contain the RGB images in JPG format and another folder should contain its corresponding anotations in XML format using http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html[PASCAL VOC^] format or TXT format using COCO format (https://cocodataset.org/#home). The RGB images and annotations should be organized as follows:
. _Object Detection Dataset:_ Two folders are demanded: the first folder should contain the RGB images in JPG format and another folder should contain its corresponding anotations in XML format using http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html[PASCAL VOC^] format or TXT format using COCO format (http://cocodataset.org/#home). The RGB images and annotations should be organized as follows:
image::object_detection.png[150,150]
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@@ -3937,7 +3920,7 @@ NOTE: Torchtext were used to preprocess and load the text input. More informatio
| Boolean (default=True)
|===
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/vision/stable/models.html[AlexNet^].
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/docs/stable/torchvision/models.html[AlexNet^].
===== DenseNet-161
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@@ -3945,7 +3928,7 @@ NOTE: PyTorch is used to build the model architecture based on https://pytorch.o
*Usage:* It should be connected to <<Train_Image_Classification_Model>>.
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/vision/stable/models.html[DenseNet-161^].
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/docs/stable/torchvision/models.html[DenseNet-161^].
.DenseNet-161_Task variables
[cols="2,5,2"]
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@@ -3965,7 +3948,7 @@ NOTE: PyTorch is used to build the model architecture based on https://pytorch.o
*Usage:* It should be connected to <<Train_Image_Classification_Model>>.
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/vision/stable/models.html[ResNet-18^].
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/docs/stable/torchvision/models.html[ResNet-18^].
.ResNet-161_Task variables
[cols="2,5,2"]
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@@ -3985,7 +3968,7 @@ NOTE: PyTorch is used to build the model architecture based on https://pytorch.o
*Usage:* It should be connected to <<Train_Image_Classification_Model>>.
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/vision/stable/models.html[VGG-16^].
NOTE: PyTorch is used to build the model architecture based on https://pytorch.org/docs/stable/torchvision/models.html[VGG-16^].
.VGG-16_Task variables
[cols="2,5,2"]
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@@ -4171,7 +4154,7 @@ NOTE: PyTorch is used to build the model architecture based on https://github.co
|Boolean(default=True)
|===
NOTE:ThedefaultparametersoftheYOLOnetworkweresetfortheCOCOdataset(https://cocodataset.org/#home).Ifyou'd like to use another dataset, you probably need to change the default parameters.
NOTE:ThedefaultparametersoftheYOLOnetworkweresetfortheCOCOdataset(http://cocodataset.org/#home).Ifyou'd like to use another dataset, you probably need to change the default parameters.
@@ -833,7 +833,7 @@ The service is started using the following variables.
| Boolean
| `false`
| `PYTHON_ENTRYPOINT`
| This entry script starts the service and defines the different functions to deploy the model, scores the prediction requests based on the deployed model, and returns the results. This script is specific to your model. This file should be stored in the Catalog under the `model_as_service_resources` bucket. More information about this file can be found in the <<../PML/PMLUserGuide.html#_customize_the_service>> section.
| This entry script starts the service and defines the different functions to deploy the model, scores the prediction requests based on the deployed model, and returns the results. This script is specific to your model. This file should be stored in the Catalog under the `model_as_service_resources` bucket. More information about this file can be found in the <<_customize_the_service>> section.
| Yes
| String
| `dl_service`
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@@ -853,7 +853,7 @@ The service is started using the following variables.
| Boolean
| `true`
| `YAML_FILE`
| A YAML file that describes the OpenAPI Specification ver. 2 (known as Swagger Spec) of the service. This file should be stored in the catalog under the `model_as_service_resources` bucket. More information about the structure of this file can be found in the section <<../PML/PMLUserGuide.html#_customize_the_service>>.
| A YAML file that describes the OpenAPI Specification ver. 2 (known as Swagger Spec) of the service. This file should be stored in the catalog under the `model_as_service_resources` bucket. More information about the structure of this file can be found in the section <<_customize_the_service>>.
@@ -105,7 +105,7 @@ The second way to start a ProActive Scheduler is to install it as a system servi
==== How to install ProActive on Windows
Under Windows, it is possible to use the https://nssm.cc/[nssm^] service manager tool to manage a running script as a service. You can configure nssm to absolve all responsibility for restarting it and let Windows take care of recovery actions.
Under Windows, it is possible to use the https://nssm.cc/[nssm^] service manager tool to manage a running script as a service. You can configure nssm to absolve all responsibility for restarting it and let Windows take care of recovery actions.
In our case, you need to provide to nssm the Path to this script `$PROACTIVE_HOME/bin/proactive-server.bat` to start ProActive as a service.