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Model Resource

Definition

You can teach our system to recognize your custom concepts. This knowledge is stored in a Model and can be used in a Visual Mining Job for these modules: Face Recognition, Object & Scene Recognition and Landmark Recognition.

There are pre-trained Models ready to use and you can train your own custom model.

ENDPOINTS

GET /v1/models/

GET /v1/models/{MODEL_ID}/

PUT /v1/models/{MODEL_ID}/

DELETE /v1/models/{MODEL_ID}/

Pre-trained Models

Model Visual Mining Module Description
celebrities Face Recognition 20.000 personalities, including the world's most famous people and a vast majority of German politicians and athletes
general-c Object & Scene Recognition Various objects and scenes, from general to more specific ones
general-b Landmark Recognition Important sights, architectural structures and natural monuments across Europe + North America
europe Landmark Recognition Important sights, architectural structures and natural monuments only across Europe

Attributes

Name Type Description
id string Global identifier to access the actual resource
name string Name of the model
description string Description or notes of the model
versions List of ModelVersion objects A list of all versions (new version is added on training)
latest_version ModelVersion object The latest version as ModelVersion object
type string Type of the model (corresponding to the type of the dataset that was used during training)
is_public boolean Whether the model is pre-trained by DeepVA (public) or custom trained
time_created string Creation time of the dataset (ISO Time String)
time_updated string Modification time of the dataset (ISO Time String)
custom_fields List of dict Custom data can be bound to the model

JSON Example

The following JSON snippet is showing a custom trained Model object.

{
  "id": "90dc4c05-9989-4fa6-a1c7-702c7eea8bcb",
  "name": "DeepVA Team",
  "description": "Recognizing the DeepVA Team",
  "type": "face",
  "is_public": false,
  "time_created": "2020-02-27 17:46:44.006000",
  "time_updated": "2020-02-27 17:46:44.006000",
  "versions": [
    {
      "version_number": 1,
      "time_created": "2020-01-01 00:00:00.786000",
      "changelog": "Initial training",
      "source_dataset": {
        "id": "812ad5e6-5e65-4de4-a02a-0484072047b0",
        "name": "Team",
        "description": "This is the DeepVA team dataset",
        "type": "face",
        "time_created": "2020-02-20 17:54:06.854000",
        "time_updated": "2020-02-20 17:54:06.854000",
        "number_of_classes": 7,
        "number_of_active_classes": 7,
        "number_of_images": 4,
        "number_of_active_images": 4,
        "preview_images": [
          "storage://JL5Uh6yZFxdRO7DEdnCY"
        ],
        "evaluation_result": {}
      }
    }
  ],
  "latest_version": {
      "version_number": 1,
      "time_created": "2020-01-01 00:00:00.786000",
      "changelog": "Initial training",
      "source_dataset": {
        "id": "812ad5e6-5e65-4de4-a02a-0484072047b0",
        "name": "Team",
        "description": "This is the DeepVA team dataset",
        "type": "face",
        "time_created": "2020-02-20 17:54:06.854000",
        "time_updated": "2020-02-20 17:54:06.854000",
        "number_of_classes": 7,
        "number_of_active_classes": 7,
        "number_of_images": 4,
        "number_of_active_images": 4,
        "preview_images": [
          "storage://JL5Uh6yZFxdRO7DEdnCY"
        ],
        "evaluation_result": {}
      }
  },
  "custom_fields": []
}