oculi

KO | EN

gitlite — search

#python #ai #typescript #android #rust #macos #java #windows #awesome #cli #awesome-list #pytorch #ios #openai

(★ 63)

A Google Cloud-based pipeline for tagging image and video ads based on their content, enabling advanced creative analysis.

oculi 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

**Note:** This is not an officially supported Google product. It is a reference
implementation.

# Oculi

Oculi is a Google Cloud-based pipeline for tagging large sets of images or
videos with labels based on their content, generating a BigQuery dataset for
further analysis. Content tagging is done through Cloud's pre-trained computer
vision models (Vision API and Video Intelligence API).

The primary use case is for analyzing creatives (images and videos) in digital
advertising. Combined with creative performance data, the output from this
pipeline can be used to explore correlations between advertising content and
performance (e.g. creatives with a human model tend to perform better).

## Creative Sources

This pipeline supports three sources of creatives [^1]:

*   **A Google Campaign Manager (CM) account.** Oculi will attempt to extract
    all creatives on the account that have an image or video asset in a suitable
    format [^2], then download the asset and save a copy to Cloud Storage. Users
    of DV360 may be able to use this option (see FAQ).
*   **A BigQuery table of URLs.** URLs must point to images or videos, and be
    accessible without login. Oculi will download the asset and save a copy to
    Cloud Storage. The required table columns are:
    *   `Creative_ID`, an unique integer for each image or video
    *   `Advertiser_ID`, an integer identifying a parent entity
    *   `Creative_Name`, a text field
    *   `Full_URL`, the URL to the image or video file
*   **A Google Cloud Storage (GCS) bucket of creative files.** Files must be
    image or video files (JPG and MP4 preferred) at the top level of the bucket.
    *   If the filenames follow the convention `{numeric_id}_{other_stuff}.jpg`,
        then the `numeric_id` will be used as the `creative_id`.
    *   Otherwise, a `creative_id` is generated by calling the Python `hash()`
        function on the entire filename.

[^1]: The term "creative" is used to indicate just the image or video content in
    an ad, excluding other components like targeting preferences.
[^2]: This excludes dynamic creatives. The goal of this pipeline is to break
    images and videos down into their content components; dynamic creatives
    are already broken into content components. Rather, analysis of the data
    from this pipeline can be used to inform a dynamic creative strategy.
    
 You could deploy Oculi in couple of ways:
 * **Colab** - Files located under colab subdirectory
 * **Dataflow** - Files located under Dataflow subdirectory

oculi

oculi 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

oculi

oculi 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README