Instance segmentation STM32 model zoo

The Instance Segmentation Model Zoo provides a collection of independent services related to machine learning for instance segmentation.

To use the services in the Instance segmentation model zoo, you can utilize the model zoo stm32ai_main.py along with user_config.yaml file as input. The yaml file specifies the service and a set of configuration parameters such as the model (either from the model zoo or your own custom model), the dataset, and the preprocessing parameters, among others.

More information about the different services and their configuration options can be found in the next section.

To use any service of this use case, you will need to update the user_config.yaml file, which specifies the parameters and configuration options for the services that you want to use. Each section of the user_config.yaml file is explained in detail in the following sections.

The operation_mode is a top-level attribute that specifies the operations or the service you want to execute.

The different values of the operation_mode attribute and the corresponding operations are described in the table below:

You can refer to the README links below that provide typical examples of operation modes and tutorials on specific services:

• prediction
• benchmarking
• deployment

Deployment on the instance segmentation is only supported on the STM32N6 device at this time.

Example and terminology:

An example of YAML code is shown below.

preprocessing:
  rescaling:
    scale: 1/127.5
    offset: -1
  resizing:
    aspect_ratio: fit
    interpolation: nearest

The code consists of a number of nested "key-value" pairs. The colon character is used as a separator between the key and the value.

Indentation is how YAML denotes nesting. The specification forbids tabs because tools treat them differently. A common practice is to use 2 or 3 spaces, but you can use any number of them.

We use "attribute-value" instead of "key-value" as in the YAML terminology, the term "attribute" being more relevant to our application. We may use the term "attribute" or "section" for nested attribute-value pairs constructs. In the example above, we may indifferently refer to "preprocessing" as an attribute (whose value is a list of nested constructs) or as a section.

Comments:

Comments begin with a pound sign. They can appear after an attribute value or take up an entire line.

preprocessing:
  rescaling:
    scale: 1/127.5   # This is a comment.
    offset: -1
  resizing:
    # This is a comment.
    aspect_ratio: fit
    interpolation: nearest
  color_mode: rgb

Attributes with no value:

The YAML language supports attributes with no value. The code below shows the alternative syntaxes you can use for such attributes.

attribute_1:
attribute_2: ~
attribute_3: null
attribute_4: None     # Model Zoo extension

The value None is a Model Zoo extension that was made because it is intuitive to Python users.

Attributes with no value can be useful to list in the configuration file all the attributes that are available in a given section and explicitly show which ones were not used.

Strings:

You can enclose strings in single or double quotes. However, unless the string contains special YAML characters, you don't need to use quotes.

This syntax:

resizing:
  aspect_ratio: fit
  interpolation: nearest

is equivalent to this one:

resizing:
  aspect_ratio: "fit"
  interpolation: "nearest"

Strings with special characters:

If a string value includes YAML special characters, you need to enclose it in single or double quotes. In the example below, the string includes the ',' character, so quotes are required.

name: "Pepper,_bell___Bacterial_spot"

Strings Spanning Multiple Lines:

You can write long strings on multiple lines for better readability. This can be done using the '|' (pipe) continuation character as shown in the example below.

This syntax:

LearningRateScheduler:
  schedule: |
    lambda epoch, lr:
        (0.0005*epoch + 0.00001) if epoch < 20 else
        (0.01 if epoch < 50 else
        (lr / (1 + 0.0005 * epoch)))

is equivalent to this one:

LearningRateScheduler:
  schedule: "lambda epoch, lr: (0.0005*epoch + 0.00001) if epoch < 20 else (0.01 if epoch < 50 else (lr / (1 + 0.0005 * epoch)))"

Note that when using the first syntax, strings that contain YAML special characters don't need to be enclosed in quotes. In the example above, the string includes the ',' character.

Booleans:

The syntaxes you can use for boolean values are shown below. Supported values have been extended to True and False in the Model Zoo as they are intuitive to Python users.

# YAML native syntax
attribute_1: true
attribute_2: false

# Model Zoo extensions
attribute_3: True
attribute_4: False

Numbers and numerical expressions:

Attribute values can be integer numbers, floating-point numbers, or numerical expressions as shown in the YAML code below.

ReduceLROnPlateau:
  patience: 10    # Integer value
  factor: 0.1     # Floating-point value
  min_lr: 1e-6    # Floating-point value, exponential notation

rescaling:
  scale: 1/127.5  # Numerical expression, evaluated to 0.00784314
  offset: -1

Lists:

You can specify lists on a single line or on multiple lines as shown below.

This syntax:

class_names: [ aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor ]

is equivalent to this one:

class_names:
  - aeroplane
  - bicycle
  - bird
  - boat
  - bottle
  - bus
  - car
  - cat
  - chair
  - cow
  - diningtable
  - dog
  - horse
  - motorbike
  - person
  - pottedplant
  - sheep
  - sofa
  - train
  - tvmonitor

Multiple attribute-value pairs on one line:

Multiple attribute-value pairs can be specified on one line as shown below.

This syntax:

rescaling: { scale: 1/127.5, offset: -1 }

is equivalent to this one:

rescaling:
  scale: 1/127.5
  offset: -1