efficientdet-d0-tf#

Use Case and High-Level Description#

The efficientdet-d0-tf model is one of the EfficientDet models designed to perform object detection. This model was pre-trained in TensorFlow*. All the EfficientDet models have been pre-trained on the Common Objects in Context (COCO) image database. For details about this family of models, check out the Google AutoML repository.

Steps to Reproduce Conversion to Frozen Graph#

  1. Clone the original repository

git clone https://github.com/google/automl.git
cd automl

  1. Checkout the commit that the conversion was tested on:

git checkout 341af7d4da7805c3a874877484e133f33c420ec5

  1. Navigate to efficientdet source code directory

cd efficientdet

  1. Install dependencies

pip install -r requirements.txt

  1. Download model checkpoint archive using this link and unzip it.

  2. Run following command:

    python model_inspect.py --runmode=saved_model --model_name=efficientdet-d0 --ckpt_path=CHECKPOINT_DIR --saved_model_dir=OUTPUT_DIR

    where CHECKPOINT_DIR - directory where model checkpoint stored, OUTPUT_DIR - directory where converted model should be stored.

Specification#

Metric

Value

Type

Object detection

GFLOPs

2.54

MParams

3.9

Source framework

TensorFlow*

Accuracy#

Metric

Converted model

COCO mAP (0.5:0.05:0.95)

31.95%

Input#

Original Model#

Image, name - image_arrays, shape - 1, 512, 512, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is RGB.

Converted Model#

Image, name - image_arrays/placeholder_port_0, shape - 1, 512, 512, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is BGR.

Output#

Original Model#

The array of summary detection information, name: detections, shape: 1, 100, 7 in the format 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, y_min, x_min, y_max, x_max, confidence, label], where:

  • image_id - ID of the image in the batch

  • (x_min, y_min) - coordinates of the top left bounding box corner

  • (x_max, y_max) - coordinates of the bottom right bounding box corner

  • confidence - confidence for the predicted class

  • label - predicted class ID, in range [1, 91], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl.txt file

Converted Model#

The array of summary detection information, name: detections, shape: 1, 1, 100, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

  • image_id - ID of the image in the batch

  • label - predicted class ID, in range [0, 90], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_91cl.txt file

  • conf - confidence for the predicted class

  • (x_min, y_min) - coordinates of the top left bounding box corner (coordinates stored in normalized format, in range [0, 1])

  • (x_max, y_max) - coordinates of the bottom right bounding box corner (coordinates stored in normalized format, in range [0, 1])

Download a Model and Convert it into OpenVINO™ IR Format#

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage#

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: