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Flash Zero

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

flash {TASK_NAME}

Here is the list of currently supported tasks.

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

flash image_classification from_folders --train_folder data/hymenoptera_data/train

3. Modify the model and training parameters

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, you could use the following command:

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

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.

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, you could use the following command:

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, so the trainer and model arguments can be configured either from the command line or from a config file. For example, to run the image classifier for 10 epochs with a resnet50 backbone you can use:

flash image_classification --trainer.max_epochs 10 --model.backbone resnet50

To view all of the available options for a task, run:

flash image_classification --help

Using Your Own Data

Flash Zero works with your own data through subcommands. The available subcommands for each task are given at the bottom of their help pages (e.g. when running flash image-classification --help). You can then use the required subcommand to train on your own data. Let’s look at an example using the Hymenoptera data from the Image Classification guide. First, download and unzip your data:

curl https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip -o hymenoptera_data
unzip hymenoptera_data.zip

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:

flash --help

This will output the following:

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:

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:

flash image_classification from_folders --help

Finally, you can generate a config.yaml file from the client to ease parameters modification by running:

flash image_classification --print_config > config.yaml
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