Getting Started & Project: Object Detection - Revision history

BPeat: Text replacement - "http:" to "https:"

2023-03-28T19:52:59Z

Text replacement - "http:" to "https:"

BPeat: /* First Project: Object Detection */

2020-07-14T21:47:52Z

‎First Project: Object Detection

BPeat: /* First Project: Object Detection */

2020-07-14T21:38:10Z

‎First Project: Object Detection

BPeat at 11:22, 4 September 2019

2019-09-04T11:22:40Z

BPeat at 17:28, 29 June 2019

2019-06-29T17:28:49Z

BPeat at 23:25, 2 February 2019

2019-02-02T23:25:08Z

BPeat at 01:09, 26 June 2018

2018-06-26T01:09:48Z

BPeat at 01:08, 26 June 2018

2018-06-26T01:08:57Z

BPeat at 20:28, 25 June 2018

2018-06-25T20:28:16Z

BPeat: /* First Project: Object Detection */

2018-06-25T20:23:18Z

‎First Project: Object Detection

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=amazon+deeplens+Object+Detection YouTube search...]
+[https://www.youtube.com/results?search_query=amazon+deeplens+Object+Detection YouTube search...]
-[http://www.google.com/search?q=amazon+deeplens+Object+Detection+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=amazon+deeplens+Object+Detection+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Image Retrieval / Object Detection]]
 * [[DeepLens - deep learning enabled video camera]]
 * [[More DeepLens Projects]]
-* [http://aws.amazon.com/deeplens/ Product page...]
+* [https://aws.amazon.com/deeplens/ Product page...]
-* [http://forums.aws.amazon.com/forum.jspa?forumID=275  DeepLens Discussion Forum | AWS]
+* [https://forums.aws.amazon.com/forum.jspa?forumID=275  DeepLens Discussion Forum | AWS]
 == I just got my DeepLens! ==
-* [http://aws.amazon.com/deeplens/resources/ DeepLens Developer Resources | AWS]
+* [https://aws.amazon.com/deeplens/resources/ DeepLens Developer Resources | AWS]
-** [http://aws.amazon.com/getting-started/tutorials/configure-aws-deeplens/ Configure Your New AWS DeepLens]
+** [https://aws.amazon.com/getting-started/tutorials/configure-aws-deeplens/ Configure Your New AWS DeepLens]
-** [http://aws.amazon.com/getting-started/tutorials/build-deeplens-project-sagemaker/?trk=gs_card Build an AWS DeepLens Project]
+** [https://aws.amazon.com/getting-started/tutorials/build-deeplens-project-sagemaker/?trk=gs_card Build an AWS DeepLens Project]
-** [http://aws.amazon.com/getting-started/tutorials/create-deploy-project-deeplens/?trk=gs_card Creating and Deploying an AWS DeepLens Project]
+** [https://aws.amazon.com/getting-started/tutorials/create-deploy-project-deeplens/?trk=gs_card Creating and Deploying an AWS DeepLens Project]
-** [http://aws.amazon.com/getting-started/tutorials/extend-deeplens-project/?trk=gs_card Extending Your AWS DeepLens Project]
+** [https://aws.amazon.com/getting-started/tutorials/extend-deeplens-project/?trk=gs_card Extending Your AWS DeepLens Project]
-* [http://blog.kloud.com.au/2018/02/27/aws-deeplens-part-1-getting-the-deeplens-online/ Part 1 - Getting Online | SAMZAKKOUR @ Kloud]
+* [https://blog.kloud.com.au/2018/02/27/aws-deeplens-part-1-getting-the-deeplens-online/ Part 1 - Getting Online | SAMZAKKOUR @ Kloud]
-* [http://www.lynda.com/Amazon-Web-Services-tutorials/Setting-up-your-AWS-DeepLens/706932/737910-4.html Lynda.com Setting up your AWS DeepLens]
+* [https://www.lynda.com/Amazon-Web-Services-tutorials/Setting-up-your-AWS-DeepLens/706932/737910-4.html Lynda.com Setting up your AWS DeepLens]
 Thoughts: Instructions are unclear on: [1] what computer to use when, [2] what network to link, [3] no signature is required on the streaming certificate which is to be loaded into each browser - file is named 'download'. There’s a known bug that prevents the DeepLens from connecting to Wi-Fi networks that have non-alphanumeric characters (e.g. spaces).
@@ Line 33: / Line 33: @@
 *[[(Deep) Residual Network (DRN) - ResNet]]
-[http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Image Retrieval / Object Detection]]; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
+[https://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Image Retrieval / Object Detection]]; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [https://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
-Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [http://console.aws.amazon.com/deeplens/ http://console.aws.amazon.com/deeplens]. AWS [[Lambda]] function renders bounding boxes around detected objects (with ≥25% confidence by default) and sends [http://www.w3schools.com/js/js_json.asp JSON]-formatted messages with detected object types and corresponding confidence levels to an AWS IoT [[MQTT]] topic. For a walkthrough...[http://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]
+Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [https://console.aws.amazon.com/deeplens/ https://console.aws.amazon.com/deeplens]. AWS [[Lambda]] function renders bounding boxes around detected objects (with ≥25% confidence by default) and sends [https://www.w3schools.com/js/js_json.asp JSON]-formatted messages with detected object types and corresponding confidence levels to an AWS IoT [[MQTT]] topic. For a walkthrough...[https://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]
 <youtube>xzFwySJYRoE</youtube>
@@ Line 45: / Line 45: @@
 == SSH to DeepLens ==
-* [http://www.deeplearning.team Deep Learning Team | Jovon Weathers]
+* [https://www.deeplearning.team Deep Learning Team | Jovon Weathers]
 <youtube>2eKjcLsBH6E</youtube>
 <youtube>HozP1t3usPM</youtube>

@@ Line 33: / Line 33: @@
 *[[(Deep) Residual Network (DRN) - ResNet]]
-[http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Object Detection]]; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
+[http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Image Retrieval / Object Detection]]; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
 Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [http://console.aws.amazon.com/deeplens/ http://console.aws.amazon.com/deeplens]. AWS [[Lambda]] function renders bounding boxes around detected objects (with ≥25% confidence by default) and sends [http://www.w3schools.com/js/js_json.asp JSON]-formatted messages with detected object types and corresponding confidence levels to an AWS IoT [[MQTT]] topic. For a walkthrough...[http://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]

@@ Line 33: / Line 33: @@
 *[[(Deep) Residual Network (DRN) - ResNet]]
-[http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Object Detection; Faster R-CNN, YOLO, SSD]]) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
+[http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Object Detection]]; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
 Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [http://console.aws.amazon.com/deeplens/ http://console.aws.amazon.com/deeplens]. AWS [[Lambda]] function renders bounding boxes around detected objects (with ≥25% confidence by default) and sends [http://www.w3schools.com/js/js_json.asp JSON]-formatted messages with detected object types and corresponding confidence levels to an AWS IoT [[MQTT]] topic. For a walkthrough...[http://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]

@@ Line 27: / Line 27: @@
 <youtube>j0DkaM4L6n4</youtube>
-<youtube>nINqpklf7Eo</youtube>
 == First Project: Object Detection ==
@@ Line 35: / Line 35: @@
 [http://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html This project] shows you how a deep learning model can detect and recognize objects in a room. The project uses the Single Shot MultiBox Detector (SSD) framework (Reference: [[Object Detection; Faster R-CNN, YOLO, SSD]]) to detect objects with a pretrained [[ResNet-50]] network on a [[MXNet]] framework. The network has been trained on the [http://host.robots.ox.ac.uk/pascal/VOC Pascal Visual Object Classes Challenge (VOC)] dataset and is capable of recognizing 20 different kinds of objects. The model takes the video stream from your AWS DeepLens device as input and labels the objects that it identifies. The project uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens device. After deploying it, you can watch your AWS DeepLens model recognize objects around you. The model is able to recognize the following objects: airplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train, and TV monitor.
-Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [http://console.aws.amazon.com/deeplens/ http://console.aws.amazon.com/deeplens]. AWS [[Lambda]] function renders bounding boxes around detected objects (with ≥25% confidence by default) and sends JSON-formatted messages with detected object types and corresponding confidence levels to an AWS IoT [[MQTT]] topic. For a walkthrough...[http://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]
+Your web browser is the interface between you and your AWS DeepLens device. You perform all of the following activities on the AWS DeepLens console using your browser, open the AWS DeepLens console at [http://console.aws.amazon.com/deeplens/ http://console.aws.amazon.com/deeplens]. AWS [[Lambda]] function renders bounding boxes around detected objects (with ≥25% confidence by default) and sends [http://www.w3schools.com/js/js_json.asp JSON]-formatted messages with detected object types and corresponding confidence levels to an AWS IoT [[MQTT]] topic. For a walkthrough...[http://github.com/awsdocs/aws-deeplens-user-guide/blob/master/doc_source/deeplens-create-deploy-sample-project.md Creating and Deploying an AWS DeepLens Sample Project | GitHub]
 <youtube>xzFwySJYRoE</youtube>

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=amazon+deeplens+Object+Detection+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[DeepLens - deep learning enabled video camera]]
 * [[More DeepLens Projects]]

← Older revision		Revision as of 23:25, 2 February 2019
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−	[http://www.youtube.com/results?search_query=amazon+deeplens YouTube search...]	+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
		+	[http://www.youtube.com/results?search_query=amazon+deeplens+Object+Detection YouTube search...]
		+	[http://www.google.com/search?q=amazon+deeplens+Object+Detection+deep+machine+learning+ML+artificial+intelligence ...Google search]

	* [[DeepLens - deep learning enabled video camera]]		* [[DeepLens - deep learning enabled video camera]]