README.md

Shape-level Classification Example

Multi-task Classification

Introduction

Multi-task Classification involves training a model to perform multiple classification tasks simultaneously. For example, a model could be trained to classify both the type of person and vehicle attributes in a single image.

Usage

Step 0: Preparation

Prepare a attributes file like attributes.json. An example is shown below:

{
    "vehicle": {
        "bodyColor": [
            "red",
            "white",
            "blue"
        ],
        "vehicleType": [
            "SUV",
            "sedan",
            "bus",
            "truck"
        ]
    },
    "person": {
        "wearsGlasses": ["yes", "no"],
        "wearsMask": ["yes", "no"],
        "clothingColor": [
            "red",
            "green",
            "blue"
        ]
    }
}

Step 1: Run the Application

python anylabeling/app.py

Step 2: Upload the Configuration File

Click on Upload -> Upload Attributes File in the top menu bar and select the prepared configuration file to upload.

For detailed output examples, refer to this file.

Multiclass & Multilabel Classification

Similar to Image-Level Classification, you can also conduct multiclass and multilabel classification for Shape-Level Annotation.

Usage

GUI Import (Recommended)

Step 0: Preparation

Prepare a flags file like label_flags.yaml. An example is shown below:

person:
  - male
  - female
helmet:
  - white
  - red
  - blue
  - yellow
  - green

Step 1: Run the Application

python anylabeling/app.py

Step 2: Upload the Configuration File

Click on Upload -> Upload Label Flags File in the top menu bar and select the prepared configuration file to upload.

Command Line Loading

Option 1: Quick Start

python anylabeling/app.py --labels person,helmet --labelflags "{'person': ['male', 'female'], 'helmet': ['white', 'red', 'blue', 'yellow', 'green']}" --validatelabel exact

Tip

The labelflags key field supports regular expressions. For instance, you can use patterns like {person-\d+: [male, tall], "dog-\d+": [black, brown, white], .*: [occluded]}.

Option 2: Using a Configuration File

python anylabeling/app.py --labels labels.txt --labelflags label_flags.yaml --validatelabel exact

For detailed output examples, refer to this file.