+ -
+
+ Object Type The model 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.
+
+
+ -
+
+ Object Environment The training data should be as close as possible to the data on which predictions are to be made. For example, if your use case involves blurry and low-resolution images (such as from a security camera), your training data should be composed of blurry, low-resolution images. In general, you should also consider providing multiple angles, resolutions, and backgrounds for your training images.
+
+
+ -
+
+ Difficulty The model generally can't predict labels that humans can't assign. So, if a human can't be trained to assign labels by looking at the image for 1-2 seconds, the model likely can't be trained to do it either.
+
+
+ -
+
+ Label Count We recommend at least 50 labels per object category for a usable model, but using 100s or 1000s would provide better results.
+
+
+ -
+
+ Image Dimensions The model resizes the image to 300x300 pixels, so keep that in mind when training the model with images where one dimension is much longer than the other.
+ {% include responsive.html image="shrink_image.png" %}
+
+
+ -
+
+ Object Size The object of interests size should be at least ~5% of the image area to be detected. For example, on the resized 300x300 pixel image the object should cover ~60x60 pixels.
+ {% include responsive.html image="small_image.png" %}
+
+
+
## Set up Cloud Annotations
+
To use Cloud Annotations just navigate to [cloud.annotations.ai](https://cloud.annotations.ai) and click **Continue with IBM Cloud**.
-
+
+{% include responsive.html image="0a.CA_login.png" %}
Once logged, if you don't have an object storage instance, it will prompt you to create one. Click **Get started** to be directed to IBM Cloud, where you can create a free object storage instance.
-
+
+{% include responsive.html image="1a.CA_no-object-storage.png" %}
You might need to re-login to IBM Cloud to create a resource.
-
+
+{% include responsive.html image="2a.IBM_login-to-create-resource.png" %}
Choose a pricing plan and click **Create**, then **Confirm** on the following popup.
-
-Once your object storage instance has been provisioned, navigate back to [cloud.annotations.ai](https://cloud.annotations.ai) and refresh the page.
+{% include responsive.html image="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.
The files and annotations will be stored in a **bucket**, You can create one by clicking **Start a new project**.
-
+
+{% include responsive.html image="4a.CA_create-bucket.png" %}
Give the bucket a unique name.
-
+{% include responsive.html image="5.CA_name-bucket.png" %}
After your bucket is created and named, it will prompt you to choose an annotation type. Choose `Classification`.
-
+{% include responsive.html image="6a.CA_set-type-classification.png" %}
## Labeling the data
-1. Create the desired labels
-
-2. Upload a video or some images
-
-3. Select images then choose `Label` > `DESIRED_LABEL`
-
+
+
+ -
+
+ Object Type The model 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.
+
+
+ -
+
+ Object Environment The training data should be as close as possible to the data on which predictions are to be made. For example, if your use case involves blurry and low-resolution images (such as from a security camera), your training data should be composed of blurry, low-resolution images. In general, you should also consider providing multiple angles, resolutions, and backgrounds for your training images.
+
+
+ -
+
+ Difficulty The model generally can't predict labels that humans can't assign. So, if a human can't be trained to assign labels by looking at the image for 1-2 seconds, the model likely can't be trained to do it either.
+
+
+ -
+
+ Label Count We recommend at least 50 labels per object category for a usable model, but using 100s or 1000s would provide better results.
+
+
+ -
+
+ Image Dimensions The model resizes the image to 300x300 pixels, so keep that in mind when training the model with images where one dimension is much longer than the other.
+ {% include responsive.html image="shrink_image.png" %}
+
+
+ -
+
+ Object Size The object of interests size should be at least ~5% of the image area to be detected. For example, on the resized 300x300 pixel image the object should cover ~60x60 pixels.
+ {% include responsive.html image="small_image.png" %}
+
+
+
## Set up Cloud Annotations
To use Cloud Annotations just navigate to [cloud.annotations.ai](https://cloud.annotations.ai) and click **Continue with IBM Cloud**.
-
+
+{% include responsive.html image="0a.CA_login.png" %}
Once logged, if you don't have an object storage instance, it will prompt you to create one. Click **Get started** to be directed to IBM Cloud, where you can create a free object storage instance.
-
+
+{% include responsive.html image="1a.CA_no-object-storage.png" %}
You might need to re-login to IBM Cloud to create a resource.
-
+
+{% include responsive.html image="2a.IBM_login-to-create-resource.png" %}
Choose a pricing plan and click **Create**, then **Confirm** on the following popup.
-
+
+{% include responsive.html image="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.
The files and annotations will be stored in a **bucket**, You can create one by clicking **Start a new project**.
-
+
+{% include responsive.html image="4a.CA_create-bucket.png" %}
Give the bucket a unique name.
-
+
+{% include responsive.html image="5.CA_name-bucket.png" %}
## [Object detection](#object-detection) or [classification](#classification)?
A classification model can tell you what an image is and how confident it is about it's decision.
@@ -55,6 +81,3 @@ If an object detection model gives us this extra information, why would we use c
* **Labor Cost** An object detection model requires humans to draw boxes around every object to train. A classification model only requires a simple label for each image.
* **Training Cost** It can take longer and require more expensive hardware to train an object detection model.
* **Inference Cost** An object detection model can be much slower than real-time to process an image on low-end hardware.
-
-
-
diff --git a/docs/_guides/training-via-gui.md b/docs/_guides/training-via-gui.md
index ab7466a..28d12d1 100644
--- a/docs/_guides/training-via-gui.md
+++ b/docs/_guides/training-via-gui.md
@@ -4,15 +4,17 @@ title: Training via GUI
---
Once you have labeled a sufficient amount of photos, click **Train Model**. A dialog message will appear, prompting you to select your Watson Machine Learning instance. If none are available, it will guide you to create a new one (You may need to refresh your Cloud Annotations window for the new instance to appear, but don't worry, your labels will be saved).
-
+
+{% include responsive.html image="wml_dialog.png" %}
Click **Train**. Your training job will not be added to the queue.
-
You will see it listed as *pending* until the training starts (this could take several minutes).
-
+
+{% include responsive.html image="pending_training.png" %}
Once your training job starts, the status will change and you will see a graph of the training steps running.
-
+
+{% include responsive.html image="training_steps.png" %}
Once the job is completed, you're all set!
\ No newline at end of file
diff --git a/docs/_guides/training-with-google-colab.md b/docs/_guides/training-with-google-colab.md
index 48cfbd0..6660c3e 100644
--- a/docs/_guides/training-with-google-colab.md
+++ b/docs/_guides/training-with-google-colab.md
@@ -17,13 +17,13 @@ You can export your data from Cloud Annotations via the following steps:
1. Choose `File` > `Export as Create ML`
-
+{% include responsive.html image="export-annotations.png" %}
## Uploading to Google Drive
Once exported, you should have a file named `