https://github.com/dkurt/openvino_efficientdet

EfficientDet with Intel OpenVINO
https://github.com/dkurt/openvino_efficientdet

automl efficientdet google intel openvino

Last synced: 5 months ago
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EfficientDet with Intel OpenVINO

• Host: GitHub
• URL: https://github.com/dkurt/openvino_efficientdet
• Owner: dkurt
• License: apache-2.0
• Created: 2020-09-10T16:46:21.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-03-14T14:01:50.000Z (almost 3 years ago)
• Last Synced: 2025-04-04T19:03:11.441Z (10 months ago)
• Topics: automl, efficientdet, google, intel, openvino
• Language: Python
• Homepage: https://github.com/openvinotoolkit/openvino
• Size: 1000 KB
• Stars: 12
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

          
# EfficientDet with Intel OpenVINO

This repository demonstrates how to convert [AutoML EfficientDet](https://github.com/google/automl) to OpenVINO IR.

Follow the steps from [.github/workflows/main.yml](.github/workflows/main.yml) to convert your model.

For public models, download IRs from [GitHub Actions](https://github.com/dkurt/openvino_efficientdet/actions?query=branch%3Amaster)

[![CI](https://github.com/dkurt/openvino_efficientdet/workflows/CI/badge.svg?branch=master)](https://github.com/dkurt/openvino_efficientdet/actions?query=branch%3Amaster)

## How to convert model

1. Freeze graph

    ```bash

    cd automl/efficientdet

    python3 model_inspect.py --runmode=saved_model --model_name=efficientdet-d4 --ckpt_path=efficientdet-d4 --saved_model_dir=savedmodeldir

    ```

2. Create IR

    ```bash

    git clone https://github.com/openvinotoolkit/openvino --depth 1

    python3 openvino/model-optimizer/mo.py \

      --input_model efficientdet-d4.pb \

      --transformations_config openvino/model-optimizer/extensions/front/tf/automl_efficientdet.json \

      --input_shape "[1, 1024, 1024, 3]"

    ```

    find resolution of your model at https://github.com/google/automl/blob/master/efficientdet/hparams_config.py

    `automl_efficientdet.json` contains topology hyper-parameters

3. Validate model comparing accuracy with an original frozen TensorFlow graph

    ```bash

    python3 scripts/validate.py --version d4 --width 1024 --height 1024

    ```