Jeremy lig 3948 make ssl docs more distinct (#1394)

closes lig-3948
- replace logos to highlight we are talking about `Lightly SSL` and not about `"Lightly"`
- replace all self-references to `lightly` with `Lightly SSL` or clarify when needed that its `Lightly Worker`
- add banner referring to the `Lightly Worker` docs
- pimp footer and add link to github and worker docs

README.md

@@ -1,25 +1,27 @@
 
-![Lightly Logo](docs/logos/lightly_logo_crop.png)
+![Lightly SSL self-supervised learning Logo](docs/logos/lightly_SSL_logo_crop.png)
 
 ![GitHub](https://img.shields.io/github/license/lightly-ai/lightly)
 ![Unit Tests](https://github.com/lightly-ai/lightly/workflows/Unit%20Tests/badge.svg)
 [![PyPI](https://img.shields.io/pypi/v/lightly)](https://pypi.org/project/lightly/)
 [![Downloads](https://static.pepy.tech/badge/lightly)](https://pepy.tech/project/lightly)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
-Lightly is a computer vision framework for self-supervised learning.
+Lightly SSL is a computer vision framework for self-supervised learning.
 
 - [Documentation](https://docs.lightly.ai/self-supervised-learning/)
 - [Github](https://github.com/lightly-ai/lightly)
 - [Discord](https://discord.gg/xvNJW94) (We have weekly paper sessions!)
 
-We also built a whole platform on top, with additional features for active learning
-and data curation. If you're interested in the platform, check out [lightly.ai](https://www.lightly.ai).
+We've also built a whole platform on top, with additional features for active learning
+and [data curation](https://docs.lightly.ai/docs/what-is-lightly). If you're interested in the
+Lightly Worker Solution to easily process millions of samples and run [powerful algorithms](https://docs.lightly.ai/docs/selection)
+on your data, check out [lightly.ai](https://www.lightly.ai). It's free to get started!
 
 
 ## Features
 
-This framework offers the following features:
+This self-supervised learning framework offers the following features:
 
 - Modular framework, which exposes low-level building blocks such as loss functions and
   model heads.

docs/source/_templates/footer.html

@@ -26,7 +26,13 @@
         {%- else %}
           {% set copyright = copyright|e %}
           <!-- Adapted to include link to website -->
-          &copy; {% trans %}Copyright{% endtrans %} {{ copyright_year }}, <a href="{{ website_url }}">{{ copyright }}</a>
+          &copy; {% trans %}Copyright{% endtrans %} {{ copyright_year }}
+          &nbsp;|&nbsp;<a href="{{ website_url }}" target="_blank">{{ copyright }}</a>
+          &nbsp;|&nbsp;<a href="https://github.com/lightly-ai/lightly" target="_blank">
+            <img width="24px" alt="Lightly SSL source code" src="data:image/svg+xml;base64,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" />
+            Source Code
+          </a>
+          &nbsp;|&nbsp;<a href="https://docs.lightly.ai" target="_blank">Lightly Worker Solution documentation</a>
         {%- endif %}
       {%- endif %}
 

docs/source/_templates/layout.html

@@ -8,6 +8,36 @@
   We need this to override the footer
 -->
 {%- block content %}
+  <style>
+    .wy-nav-content-wrap {
+      background: #f0f0f0;
+    }
+    .wy-nav-content{
+      position: relative;
+    }
+    .lighlty-worker-banner{
+      background: #092643;
+      height: 40px;
+      width: 100%;
+      position: absolute;
+      padding: 0 3.236em;
+      color: #fcfcfc; /* would be the actual text on dark color -> #d9d9d9 */
+      top: 0;
+      left: 0;
+      display: flex;
+      align-items: center;
+      z-index: 9999;
+    }
+    .lighlty-worker-banner a{
+      color: #2CC2BD;
+    }
+    .rst-content{
+      margin-top: 40px;
+    }
+  </style>
+  <div class="lighlty-worker-banner">
+    <span>Looking to easily do active learning on millions of samples? See our <a href="http://docs.lightly.ai" target="_blank">Lighly Worker</a> docs.</span>
+  </div>
   {% if theme_style_external_links|tobool %}
   <div class="rst-content style-external-links">
   {% else %}

docs/source/conf.py

@@ -22,10 +22,10 @@
 # -- Project information -----------------------------------------------------
 
 project = "lightly"
-copyright_year = "2020"
+copyright_year = "2020-<script>document.write((new Date()).getFullYear())</script>"
 copyright = "Lightly AG"
 website_url = "https://www.lightly.ai/"
-author = "Philipp Wirth, Igor Susmelj"
+author = "Lightly Team"
 
 # The full version, including alpha/beta/rc tags
 release = lightly.__version__
@@ -98,7 +98,7 @@
 
 html_favicon = "favicon.png"
 
-html_logo = "../logos/lightly_logo_crop_white_text.png"
+html_logo = "../logos/lightly_SSL_logo_crop_white_text.png"
 
 # Exposes variables so that they can be used by django
 html_context = {

docs/source/docker/advanced/datapool.rst

@@ -3,7 +3,7 @@
 Datapool
 =================
 
-Lightly has been designed in a way that you can incrementally build up a 
+Lightly Worker has been designed in a way that you can incrementally build up a 
 dataset for your project. The software automatically keeps track of the 
 representations of previously selected samples and uses this information 
 to pick new samples in order to maximize the quality of the final dataset. 
@@ -43,7 +43,7 @@ has the following advantages:
 If you want to search all data in your bucket for new samples
 instead of only newly added data,
 then set :code:`'datasource.process_all': True` in your worker config. This has the
-same effect as creating a new Lightly dataset and running the Lightly Worker from scratch
+same effect as creating a new dataset and running the Lightly Worker from scratch
 on the full dataset. We process all data instead of only the newly added ones.
 
 
@@ -67,7 +67,7 @@ first time.
     |-- passageway1-c1.avi
     `-- terrace1-c0.avi
 
-Let's create a Lightly dataset which uses that bucket (choose your tab - S3, GCS or Azure):
+Let's create a dataset which uses that bucket (choose your tab - S3, GCS or Azure):
 
 .. tabs::
   .. tab:: AWS S3 Datasource

docs/source/docker/advanced/datasource_metadata.rst

@@ -3,7 +3,7 @@
 Add Metadata to a Datasource
 ===============================
 
-Lightly can make use of metadata collected alongside your images or videos. Provided,
+Lightly Worker can make use of metadata collected alongside your images or videos. Provided,
 metadata can be used to steer the selection process and to analyze the selected dataset
 in the Lightly Platform.
 
@@ -45,7 +45,7 @@ Metadata Schema
 The schema defines the format of the metadata and helps the Lightly Platform to correctly identify 
 and display different types of metadata.
 
-You can provide this information to Lightly by adding a `schema.json` to the 
+You can provide this information to Lightly Worker by adding a `schema.json` to the 
 `.lightly/metadata` directory. The `schema.json` file must contain a list of
 configuration entries. Each of the entries is a dictionary with the following keys:
 
@@ -105,9 +105,9 @@ of the images we have collected. A possible schema could look like this:
 
 Metadata Files
 --------------
-Lightly requires a single metadata file per image or video. If an image or video has no corresponding metadata file,
-Lightly assumes the default value from the `schema.json`. If a metadata file is provided for a full video,
-Lightly assumes that the metadata is valid for all frames in that video.
+Lightly Worker requires a single metadata file per image or video. If an image or video has no corresponding metadata file,
+Lightly Worker assumes the default value from the `schema.json`. If a metadata file is provided for a full video,
+Lightly Worker assumes that the metadata is valid for all frames in that video.
 
 To provide metadata for an image or a video, place a metadata file with the same name
 as the image or video in the `.lightly/metadata` directory but change the file extension to
@@ -130,8 +130,8 @@ as the image or video in the `.lightly/metadata` directory but change the file e
 
 
 When working with videos it's also possible to provide metadata on a per-frame basis.
-Then, Lightly requires a metadata file per frame. If a frame has no corresponding metadata file,
-Lightly assumes the default value from the `schema.json`. Lightly uses a naming convention to
+Then, Lightly Worker requires a metadata file per frame. If a frame has no corresponding metadata file,
+Lightly Worker assumes the default value from the `schema.json`. Lightly Worker uses a naming convention to
 identify frames: The filename of a frame consists of the video filename, the frame number 
 (padded to the length of the number of frames in the video), the video format separated
 by hyphens. For example, for a video with 200 frames, the frame number will be padded

docs/source/docker/advanced/datasource_predictions.rst

@@ -3,9 +3,9 @@
 Add Predictions to a Datasource
 ===============================
 
-Lightly can not only use images you provided in a datasource, but also predictions of a ML model on your images.
+Lightly Worker can not only use images you provided in a datasource, but also predictions of a ML model on your images.
 They are used for active learning for selecting images based on the objects in them.
-Furthermore, object detection predictions can be used running Lightly on object level.
+Furthermore, object detection predictions can be used running Lightly Worker on object level.
 By providing the predictions in the datasource,
 you have full control over them and they scale well to millions of samples.
 Furthermore, if you add new samples to your datasource, you can simultaneously
@@ -62,8 +62,8 @@ and an object detection task). All of the files are explained in the next sectio
 
 Prediction Tasks
 ----------------
-To let Lightly know what kind of prediction tasks you want to work with, Lightly
-needs to know their names. It's very easy to let Lightly know which tasks exist:
+To let the Lightly Worker know what kind of prediction tasks you want to work with, the Lightly Worker
+needs to know their names. It's very easy to let the Lightly Worker know which tasks exist:
 simply add a `tasks.json` in your lightly bucket stored at the subdirectory `.lightly/predictions/`.
 
 The `tasks.json` file must include a list of your task names which must match name
@@ -116,7 +116,7 @@ we can specify which subfolders contain relevant predictions in the `tasks.json`
 
 Prediction Schema
 -----------------
-For Lightly it's required to store a prediction schema. The schema defines the
+It's required to store a prediction schema. The schema defines the
 format of the predictions and helps the Lightly Platform to correctly identify 
 and display classes. It also helps to prevent errors as all predictions which 
 are loaded are validated against this schema.
@@ -127,7 +127,7 @@ all the categories and their corresponding ids. For other tasks, such as keypoin
 detection, it can be useful to store additional information like which keypoints 
 are connected with each other by an edge.
 
-You can provide all this information to Lightly by adding a `schema.json` to the 
+You can provide all this information to the Lightly Worker by adding a `schema.json` to the 
 directory of the respective task. The schema.json file must have a key `categories` 
 with a corresponding list of categories following the COCO annotation format.
 It must also have a key `task_type` indicating the type of the predictions. 
@@ -167,10 +167,10 @@ The three classes are sunny, clouded, and rainy.
 
 Prediction Files
 ----------------
-Lightly requires a **single prediction file per image**. The file should be a .json
+The Lightly Worker requires a **single prediction file per image**. The file should be a .json
 following the format defined under :ref:`prediction-format` and stored in the subdirectory
 `.lightly/predictions/${TASK_NAME}` in the storage bucket the dataset was configured with.
-In order to make sure Lightly can match the predictions to the correct source image,
+In order to make sure the Lightly Worker can match the predictions to the correct source image,
 it's necessary to follow the naming convention:
 
 .. code-block:: bash
@@ -189,7 +189,7 @@ it's necessary to follow the naming convention:
 
 Prediction Files for Videos
 ---------------------------
-When working with videos, Lightly requires a prediction file per frame. Lightly
+When working with videos, the Lightly Worker requires a prediction file per frame. Lightly
 uses a naming convention to identify frames: The filename of a frame consists of
 the video filename, the video format, and the frame number (padded to the length
 of the number of frames in the video) separated by hyphens. For example, for a
@@ -363,7 +363,7 @@ belonging to that category. Optionally, a list of probabilities can be provided
 containing a probability for each category, indicating the likeliness that the
 segment belongs to that category.
 
-To kickstart using Lightly with semantic segmentation predictions we created an 
+To kickstart using the Lightly Worker with semantic segmentation predictions we created an 
 example script that takes model predictions and converts them to the correct 
 format :download:`semantic_segmentation_inference.py <code_examples/semantic_segmentation_inference.py>` 
 
@@ -403,13 +403,13 @@ following function:
 
 Segmentation models oftentimes output a probability for each pixel and category.
 Storing such probabilities can quickly result in large file sizes if the input
-images have a high resolution. To reduce storage requirements, Lightly expects 
+images have a high resolution. To reduce storage requirements, Lightly Worker expects 
 only a single score or probability per segmentation. If you have scores or 
 probabilities for each pixel in the image, you have to first aggregate them 
 into a single score/probability. We recommend to take either the median or mean 
 score/probability over all pixels within the segmentation mask. The example
 below shows how pixelwise segmentation predictions can be converted to the 
-format required by Lightly.
+format required by the Lightly Worker.
 
 .. code-block:: python
 
@@ -522,7 +522,7 @@ Don't forget to change these 2 parameters at the top of the script.
 Creating Prediction Files for Videos
 -------------------------------------
 
-Lightly expects one prediction file per frame in a video. Predictions can be 
+The Lightly Worker expects one prediction file per frame in a video. Predictions can be 
 created following the Python example code below. Make sure that `PyAV <https://pyav.org/>`_ 
 is installed on your system for it to work correctly.
 

docs/source/docker/advanced/load_model_from_checkpoint.rst

@@ -3,8 +3,8 @@
 Load Model from Checkpoint
 ==========================
 
-The Lightly worker can be used to :ref:`train a self-supervised model on your data. <training-a-self-supervised-model>`
-Lightly saves the weights of the model after training to a checkpoint file in
+The Lightly Worker can be used to :ref:`train a self-supervised model on your data. <training-a-self-supervised-model>`
+Lightly Worker saves the weights of the model after training to a checkpoint file in
 :code:`output_dir/lightly_epoch_X.ckpt`. This checkpoint can then be further
 used to, for example, train a classifier model on your dataset. The code below
 demonstrates how the checkpoint can be loaded:

docs/source/docker/advanced/object_level.rst

@@ -2,7 +2,7 @@
 
 Object Level
 ============
-Lightly does not only work on full images but also on an object level. This
+The Lightly Worker does not only work on full images but also on an object level. This
 workflow is especially useful for datasets containing small objects or multiple
 objects in each image and provides the following benefits over the full image
 workflow:
@@ -21,7 +21,7 @@ workflow:
 
 Prerequisites
 -------------
-In order to use the object level workflow with Lightly, you will need the
+In order to use the object level workflow with the Lightly Worker, you will need the
 following things:
 
 - The installed Lightly Worker (see :ref:`docker-setup`)
@@ -31,13 +31,13 @@ following things:
 
 .. note::
 
-    If you don't have any predictions available, you can use the Lightly pretagging
+    If you don't have any predictions available, you can use the Lightly Worker pretagging
     model. See :ref:`Pretagging <object-level-pretagging>` for more information.
 
 
 Predictions
 -----------
-Lightly needs to know which objects to process. This information is provided
+The Lightly Worker needs to know which objects to process. This information is provided
 by uploading a set of object predictions to the datasource (see :ref:`docker-datasource-predictions`).
 Let's say we are working with a dataset containing different types of vehicles
 and used an object detection model to find possible vehicle objects in the
@@ -170,7 +170,7 @@ code to sping up a Lightly Worker
 
 Padding
 -------
-Lightly makes it possible to add a padding around your bounding boxes. This allows
+The Lightly Worker makes it possible to add a padding around your bounding boxes. This allows
 for better visualization of the cropped images in the web-app and can improve the
 embeddings of the objects as the embedding model sees the objects in context. To add
 padding, simply specify `object_level.padding=X` where `X` is the padding relative
@@ -239,9 +239,9 @@ properties of your dataset and reveal things like:
 
 These hidden biases are hard to find in a dataset if you only rely on full
 images or the coarse vehicle type predicted by the object detection model.
-Lightly helps you to identify them quickly and assists you in monitoring and
+The Lightly Worker helps you to identify them quickly and assists you in monitoring and
 improving the quality of your dataset. After an initial exploration you can now
-take further steps to enhance the dataset using one of the workflows Lightly
+take further steps to enhance the dataset using one of the workflows the Lightly Worker
 provides:
 
 - Select a subset of your data using our :ref:`Sampling Algorithms <plaform-sampling>`
@@ -252,7 +252,7 @@ provides:
 Multiple Object Level Runs
 --------------------------
 You can run multiple object level workflows using the same dataset. To start a
-new run, please select your original full image dataset in the Lightly Web App
+new run, please select your original full image dataset in the Lightly Platform
 and schedule a new run from there. If you are running the Lightly Worker from Python or
 over the API, you have to set the `dataset_id` configuration option to the id of 
 the original full image dataset. In both cases make sure that the run is *not*
@@ -261,7 +261,7 @@ started from the crops dataset as this is not supported!
 You can control to which crops dataset the newly selected object crops are
 uploaded by setting the `object_level.crop_dataset_name` configuration option.
 By default this option is not set and if you did not specify it in the first run,
-you can also omit it in future runs. In this case Lightly will automatically 
+you can also omit it in future runs. In this case the Lightly Worker will automatically 
 find the existing crops dataset and add the new crops to it. If you want to
 upload the crops to a new dataset or have set a custom crop dataset name in a
 previous run, then set the `object_level.crop_dataset_name` option to a new

docs/source/docker/advanced/overview.rst

@@ -1,6 +1,6 @@
 Advanced
 ===================================
-Here you learn more advanced usage patterns of Lightly Docker.
+Here you learn more advanced usage patterns of Lightly Worker.
 
 
 .. toctree::