Update flash_zero.rst (#796)

Co-authored-by: tchaton <thomas@grid.ai>

docs/source/general/flash_zero.rst

@@ -4,15 +4,104 @@
 Flash Zero
 **********
 
-Flash Zero is a zero-code machine learning platform built directly into lightning-flash.
-To get started and view the available tasks, run:
+Flash Zero is a zero-code machine learning platform.
+Here's an image classification example to illustrate with one of the dozens tasks available.
+
+
+Flash Zero in 3 steps
+_____________________
+
+1. Select your task
+===================
 
 .. code-block:: bash
 
-    flash --help
+    flash {TASK_NAME}
+
+Here is the list of currently supported tasks.
+
+.. code-block:: bash
+
+    audio_classification     Classify audio spectrograms.
+    graph_classification     Classify graphs.
+    image_classification     Classify images.
+    instance_segmentation    Segment object instances in images.
+    keypoint_detection       Detect keypoints in images.
+    object_detection         Detect objects in images.
+    pointcloud_detection     Detect objects in point clouds.
+    pointcloud_segmentation  Segment objects in point clouds.
+    question_answering       Extractive Question Answering.
+    semantic_segmentation    Segment objects in images.
+    speech_recognition       Speech recognition.
+    style_transfer           Image style transfer.
+    summarization            Summarize text.
+    tabular_classification   Classify tabular data.
+    text_classification      Classify text.
+    translation              Translate text.
+    video_classification     Classify videos.
+
+
+2. Pass in your own data
+========================
+
+.. code-block:: bash
+
+    flash image_classification from_folders --train_folder data/hymenoptera_data/train
+
+
+3. Modify the model and training parameters
+===========================================
+
+.. code-block:: bash
+
+    flash image_classification --trainer.max_epochs 10 --model.backbone resnet50 from_folders --train_folder data/hymenoptera_data/train
+
+.. note::
+
+    The trainer and model arguments should be placed before the ``source`` subcommand. Here it is ``from_folders``.
+
+
+Other Examples
+______________
+
+Image Object Detection
+======================
+
+To train an Object Detector on `COCO 2017 dataset <https://cocodataset.org/>`_, you could use the following command:
+
+.. code-block:: bash
+
+    flash object_detection from_coco --train_folder data/coco128/images/train2017/ --train_ann_file data/coco128/annotations/instances_train2017.json --val_split .3 --batch_size 8 --num_workers 4
+
+
+Image Object Segmentation
+=========================
+
+To train an Image Segmenter on `CARLA driving simulator dataset <http://carla.org/>`_
+
+.. code-block:: bash
 
-Customize Trainer and Model Arguments
-_____________________________________
+    flash semantic_segmentation from_folders --train_folder data/CameraRGB --train_target_folder data/CameraSeg --num_classes 21
+
+Below is an example where the head, the backbone and its pretrained weights are customized.
+
+.. code-block:: bash
+
+    flash semantic_segmentation --model.head fpn --model.backbone efficientnet-b0 --model.pretrained advprop from_folders --train_folder data/CameraRGB --train_target_folder data/CameraSeg --num_classes 21
+
+Video Classification
+====================
+
+To train an Video Classifier on the `Kinetics dataset <https://deepmind.com/research/open-source/kinetics>`_, you could use the following command:
+
+
+.. code-block:: bash
+
+    flash video_classification from_folders --train_folder data/kinetics/train/ --clip_duration 1 --num_workers 0
+
+
+CLI options
+___________
 
 Flash Zero is built on top of the
 `lightning CLI <https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_cli.html>`_, so the trainer and
@@ -48,9 +137,54 @@ Now train with Flash Zero:
 
     flash image_classification from_folders --train_folder ./hymenoptera_data/train
 
+Getting Help
+____________
+
+To find all available tasks, you can run:
+
+.. code-block:: bash
+
+    flash --help
+
+This will output the following:
+
+.. code-block:: bash
+
+    Commands:
+    audio_classification     Classify audio spectrograms.
+    graph_classification     Classify graphs.
+    image_classification     Classify images.
+    instance_segmentation    Segment object instances in images.
+    keypoint_detection       Detect keypoints in images.
+    object_detection         Detect objects in images.
+    pointcloud_detection     Detect objects in point clouds.
+    pointcloud_segmentation  Segment objects in point clouds.
+    question_answering       Extractive Question Answering.
+    semantic_segmentation    Segment objects in images.
+    speech_recognition       Speech recognition.
+    style_transfer           Image style transfer.
+    summarization            Summarize text.
+    tabular_classification   Classify tabular data.
+    text_classification      Classify text.
+    translation              Translate text.
+    video_classification     Classify videos.
+
+
+To get more information about a specific task, you can do the following:
+
+.. code-block:: bash
+
+    flash image_classification --help
+
 You can view the help page for each subcommand. For example, to view the options for training an image classifier from
 folders, you can run:
 
 .. code-block:: bash
 
     flash image_classification from_folders --help
+
+Finally, you can generate a `config.yaml` file from the client to ease parameters modification by running:
+
+.. code-block:: bash
+
+    flash image_classification --print_config > config.yaml