retinanet-tf

Use Case and High-Level Description

RetinaNet is the dense object detection model with ResNet50 backbone, originally trained on Keras*, then converted to TensorFlow* protobuf format. For details, see paper, repository.

Steps to Reproduce Keras* to TensorFlow* Conversion

  1. Clone and install the original repository (tested on 47fdf189 commit) following the installation steps. Make sure you ran python setup.py build_ext --inplace from the root of repository.

  2. Download the original model from here

  3. Get conversion script:

    1. Get conversion script from repository :

      git clone https://github.com/amir-abdi/keras_to_tensorflow.git

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

      git checkout c841508a88faa5aa1ffc7a4947c3809ea4ec1228

    3. Apply keras_to_tensorflow.patch from <omz_dir>/models/public/retinanet-tf :

      git apply keras_to_tensorflow.patch

    4. Run script:

      python keras_to_tensorflow.py --input_model=<model_in>.h5 --output_model=<model_out>.pb

      Note

      The conversion was tested with keras==2.3.1, tensorflow==1.15 and h5py<3.0.0.

Metric

Value

Type

Object detection

GFlops

238.9469

MParams

64.9706

Source framework

TensorFlow*

Accuracy

Metric

Value

coco_precision

33.15%

Input

Original Model

Image, name: input_1, shape: 1, 1333, 1333, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: BGR. Mean values: [103.939, 116.779, 123.68]

Converted Model

Image, name: input_1, shape: 1, 1333, 1333, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: BGR.

Output

Original Model

  1. Classifier, name: filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3. Contains predicted bounding boxes classes in a range [1, 80]. The model was trained on Common Objects in Context (COCO) dataset version with 80 categories of objects, 0 class is for background. Mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl_bkgr.txt file

  2. Probability, name: filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3. Contains probability of detected bounding boxes.

  3. Detection box, name: filtered_detections/map/TensorArrayStack/TensorArrayGatherV3. Contains detection boxes coordinates in a format [y_min, x_min, y_max, x_max], where (x_min, y_min) are coordinates of the top left corner, (x_max, y_max) are coordinates of the right bottom corner. Coordinates are rescaled to input image size.

Converted Model

The array of summary detection information, name - DetectionOutput, shape - 1, 1, 300, 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 [1, 80], mapping to class names provided in <omz_dir>/data/dataset_classes/coco_80cl_bkgr.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: