classification redirects and fix broken images

cloud-annotations · Jan 13, 2020 · 4220a99 · 4220a99
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diff --git a/docs/_classification/2.md b/docs/_classification/2.md
@@ -1,6 +1,8 @@
 ---
 title: Creating an object storage instance
 date: 1970-01-02
+redirect_from:
+  - /classification/cli/2.html
 ---
 Creating a Cloud Object Storage instance gives us a reliable place to keep our training data. It also opens up the potential for data collection and collaboration, letting us collect user data and allowing a team of specialists to easily label it.
 

diff --git a/docs/_classification/3.md b/docs/_classification/3.md
@@ -1,6 +1,8 @@
 ---
 title: Creating a machine learning instance
 date: 1970-01-03
+redirect_from:
+  - /classification/cli/3.html
 ---
 Creating a Watson Machine Leaning instance gives us a reliable place to train, deploy and test machine learning models on specialized infrastructure. This allows us to set off several experiments simultaneously, taking advantage of multiple high-end GPUs. Watson Machine Learning supports a wide collection of machine learning frameworks such as TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS.
 

diff --git a/docs/_classification/5.md b/docs/_classification/5.md
@@ -1,6 +1,8 @@
 ---
 title: Preparing training data
 date: 1970-01-05
+redirect_from:
+  - /classification/cli/5.html
 ---
 Since we are doing image classification, the data we prepare will consist of images with associated labels.
 

diff --git a/docs/_classification/7.md b/docs/_classification/7.md
@@ -1,6 +1,8 @@
 ---
 title: Training a model
 date: 1970-01-07
+redirect_from:
+  - /classification/cli/7.html
 ---
 After we have collected and labeled our first round of images, we are ready to start training our model! 
 

diff --git a/docs/_classification/8.md b/docs/_classification/8.md
@@ -1,6 +1,8 @@
 ---
 title: Using a model
 date: 1970-01-08
+redirect_from:
+  - /classification/cli/8.html
 ---
 ## Compatable demos
 ### [Run Classification Inferences in iOS](https://github.com/cloud-annotations/classification-ios/)

diff --git a/docs/_classification/9.md b/docs/_classification/9.md
@@ -1,6 +1,8 @@
 ---
 title: Common issues
 date: 1970-01-09
+redirect_from:
+  - /classification/cli/9.html
 ---
 This page is updated regularly with issues that frequently occur.
 > **Note:** If you don't see your issue listed below, feel free to contact me at bourdakos1@gmail.com
diff --git a/docs/_classification/index.md b/docs/_classification/index.md
@@ -1,6 +1,8 @@
 ---
 title: Introduction
 date: 1970-01-01
+redirect_from:
+  - /classification/cli/
 ---
 
 ![](https://d2mxuefqeaa7sj.cloudfront.net/s_50BD1551C2CA022B9CF9D8DF0A28275DB7ACF3DBDD5764C0CB12B3AF3B1E0766_1541978358303_schematic2.png)

diff --git a/docs/_classification/what_next.md b/docs/_classification/what_next.md
@@ -1,6 +1,8 @@
 ---
 title: What next?
 date: 1970-01-11
+redirect_from:
+  - /classification/cli/what_next.html
 ---
 
 Congratulations, you've made it!

diff --git a/docs/_guides/2.md b/docs/_guides/2.md
@@ -11,27 +11,27 @@ Using an object storage gives us a reliable place to keep our training data. It
 IBM Cloud offers a lite tier of object storage, which includes 25 GB of storage for free. (this is what we will be using throughout the tutorial)
 
 To use Cloud Annotations just navigate to [cloud.annotations.ai](https://cloud.annotations.ai) and click **Continue with IBM Cloud**.
-![](assets/0a.CA_login.png)
+![](/assets/images/0a.CA_login.png)
 
 Once logged, if we don't have an object storage instance, it will prompt us to create one. Click **Get started** to be directed to IBM Cloud, where you can create a free object storage instance.
-![](assets/1a.CA_no-object-storage.png)
+![](/assets/images/1a.CA_no-object-storage.png)
 
 You might need to re-login to IBM Cloud to create a resource.
-![](assets/2a.IBM_login-to-create-resource.png)
+![](/assets/images/2a.IBM_login-to-create-resource.png)
 
 Choose a pricing plan and click **Create**, then **Confirm** on the following popup.
-![](assets/3a.IBM_create-object-storage.png)
+![](/assets/images/3a.IBM_create-object-storage.png)
 
 Once your object storage instance has been provisioned, navigate back to [cloud.annotations.ai](https://cloud.annotations.ai) and refresh the page. 
 
 We will be storing our files and annotations in something called a **bucket**, we can create one by clicking **Start a new  project**.
-![](assets/4a.CA_create-bucket.png)
+![](/assets/images/4a.CA_create-bucket.png)
 
 Give the bucket a unique name.
-![](assets/5.CA_name-bucket.png)
+![](/assets/images/5.CA_name-bucket.png)
 
 After we create and name our bucket, it will prompt us to choose an annotation type. We need to choose **Localization**. This allows us to draw bounding box rectangles on our images.
-![](assets/6a.CA_set-type.png)
+![](/assets/images/6a.CA_set-type.png)
 
 ## Training data best practices
 * The model we will be training is optimized for photographs of objects in the real world. They are unlikely to work well for x-rays, hand drawings, scanned documents, receipts, etc.
@@ -43,15 +43,15 @@ After we create and name our bucket, it will prompt us to choose an annotation t
 * We recommend at least 50 training images per label for a usable model, but using 100s or 1000s would provide better results.
 
 * The model we will be training resizes the image to 300x300 pixels, so keep that is mind when training the model with images where one dimension is much longer than the other.
-![](assets/image_shrink.png)
+![](/assets/images/image_shrink.png)
 
 ## Labeling the data
 1. Upload a video or many images
-![](assets/7a.CA_blank-canvas.png)
+![](/assets/images/7a.CA_blank-canvas.png)
 2. Create the desired labels
-![](assets/9a.CA_create-label.png)
+![](/assets/images/9a.CA_create-label.png)
 3. Start drawing bounding boxes
-![](assets/10.CA_labeled.png)
+![](/assets/images/10.CA_labeled.png)
 
 ## &nbsp;
 > **📁 [Sample Training Data](https://ibm.box.com/v/counting-cars-training)**
diff --git a/docs/_guides/5.md b/docs/_guides/5.md
@@ -7,12 +7,12 @@ Creating a Watson Machine Leaning instance gives us a reliable place to train, d
 IBM Cloud offers a lite tier of Watson Machine Leaning, which includes 50 hours of compute for free per month. (this is what we will be using throughout the tutorial)
 
 To create a Watson Machine Learning instance, navigate back to your [IBM Cloud Dashboard](https://ibm.biz/cloud-annotations-sign-up) and click the **Create resource** button.
-![](https://d2mxuefqeaa7sj.cloudfront.net/s_E7D1C1E8D801F89315B72C10AD83AE795982C7EB84F7BA48CECD8A576B02D6CC_1539804040052_Screen+Shot+2018-10-17+at+2.35.53+PM.png)
+![](/assets/images/empty_dashboard.png)
 
 Locate and choose the **Watson Machine Learning** option.
-![](assets/wml_catalog.png)
+![](/assets/images/wml_catalog.png)
 
 Choose a pricing plan and click **Create**, then **Confirm** on the following popup.
 > **Note:** Cloud Annotations relies on Deep Learning as a Service which is only supported in the `Dallas` and `London` regions.
 
-![](assets/wml_create.png)
+![](/assets/images/wml_create.png)
diff --git a/docs/_guides/assets/11.CA_export.png b/docs/_guides/assets/11.CA_export.png
diff --git a/docs/_guides/assets/12.CACLI_train.png b/docs/_guides/assets/12.CACLI_train.png
diff --git a/docs/_guides/assets/credentials_hmac.png b/docs/_guides/assets/credentials_hmac.png
diff --git a/docs/_guides/assets/resources.png b/docs/_guides/assets/resources.png
diff --git a/docs/_guides/assets/wml_credentials_modal.png b/docs/_guides/assets/wml_credentials_modal.png
diff --git a/docs/_guides/assets/wml_new_credential.png b/docs/_guides/assets/wml_new_credential.png
diff --git a/docs/_guides/assets/wml_view_credentials.png b/docs/_guides/assets/wml_view_credentials.png
diff --git a/docs/_guides/index.md b/docs/_guides/index.md
@@ -3,11 +3,11 @@ title: Introduction
 date: 1970-01-01
 ---
 
-![](assets/main.png)
+![](/assets/images/main.png)
 
 ## What you will build
 In this workshop, you’ll build an app that lets you use your own custom-trained models to detect objects. You’ll create an IBM Cloud Object Storage instance to store your labeled data, then after your data is ready, you’ll learn how to start a Watson Machine Learning instance to train your own custom model on top-of-the-line GPUs. After your model has completed training, you can simply plug the model into your application.
-![](assets/main_image.png)
+![](/assets/images/main_image.png)
 
 ## Prerequisites
 * **Recommended:** A basic understanding of using terminal
diff --git a/docs/_guides/assets/0a.CA_login.png → docs/assets/images/0a.CA_login.png b/docs/_guides/assets/0a.CA_login.png → docs/assets/images/0a.CA_login.png
diff --git a/docs/_guides/assets/10.CA_labeled.png → docs/assets/images/10.CA_labeled.png b/docs/_guides/assets/10.CA_labeled.png → docs/assets/images/10.CA_labeled.png
diff --git a/...guides/assets/1a.CA_no-object-storage.png → ...assets/images/1a.CA_no-object-storage.png b/...guides/assets/1a.CA_no-object-storage.png → ...assets/images/1a.CA_no-object-storage.png
diff --git a/...ssets/2a.IBM_login-to-create-resource.png → ...mages/2a.IBM_login-to-create-resource.png b/...ssets/2a.IBM_login-to-create-resource.png → ...mages/2a.IBM_login-to-create-resource.png
diff --git a/...s/assets/3a.IBM_create-object-storage.png → ...s/images/3a.IBM_create-object-storage.png b/...s/assets/3a.IBM_create-object-storage.png → ...s/images/3a.IBM_create-object-storage.png
diff --git a/docs/_guides/assets/4a.CA_create-bucket.png → docs/assets/images/4a.CA_create-bucket.png b/docs/_guides/assets/4a.CA_create-bucket.png → docs/assets/images/4a.CA_create-bucket.png
diff --git a/docs/_guides/assets/5.CA_name-bucket.png → docs/assets/images/5.CA_name-bucket.png b/docs/_guides/assets/5.CA_name-bucket.png → docs/assets/images/5.CA_name-bucket.png
diff --git a/docs/_guides/assets/6a.CA_set-type.png → docs/assets/images/6a.CA_set-type.png b/docs/_guides/assets/6a.CA_set-type.png → docs/assets/images/6a.CA_set-type.png
diff --git a/docs/_guides/assets/7a.CA_blank-canvas.png → docs/assets/images/7a.CA_blank-canvas.png b/docs/_guides/assets/7a.CA_blank-canvas.png → docs/assets/images/7a.CA_blank-canvas.png
diff --git a/docs/_guides/assets/9a.CA_create-label.png → docs/assets/images/9a.CA_create-label.png b/docs/_guides/assets/9a.CA_create-label.png → docs/assets/images/9a.CA_create-label.png
diff --git a/docs/assets/images/empty_dashboard.png b/docs/assets/images/empty_dashboard.png
diff --git a/docs/_guides/assets/image_shrink.png → docs/assets/images/image_shrink.png b/docs/_guides/assets/image_shrink.png → docs/assets/images/image_shrink.png
diff --git a/docs/_guides/assets/main.png → docs/assets/images/main.png b/docs/_guides/assets/main.png → docs/assets/images/main.png
diff --git a/docs/_guides/assets/main_image.png → docs/assets/images/main_image.png b/docs/_guides/assets/main_image.png → docs/assets/images/main_image.png
diff --git a/docs/_guides/assets/wml_catalog.png → docs/assets/images/wml_catalog.png b/docs/_guides/assets/wml_catalog.png → docs/assets/images/wml_catalog.png
diff --git a/docs/_guides/assets/wml_create.png → docs/assets/images/wml_create.png b/docs/_guides/assets/wml_create.png → docs/assets/images/wml_create.png