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 the original repository(tested on 47fdf189 commit)
  2. Download the original model from here
  3. Get conversion script:
    1. Get conversion script from repository: ```sh git clone ```
    1. (Optional) Checkout the commit that the conversion was tested on: ``` git checkout c841508a88faa5aa1ffc7a4947c3809ea4ec1228 ``
    1. Applykeras_to_tensorflow.patch<tt>: `` git apply keras_to_tensorflow.patch ```
    1. Run script: ``` python –input_model=<model_in>.h5 –output_model=<model_out>.pb ```



Metric Value
Type Object detection
GFlops 238.9469
MParams 64.9706
Source framework TensorFlow*


Metric Value
coco_precision 33.15%



Original Model

Image, name: input_1, shape: [1x1333x1333x3], format: [BxHxWxC], 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: [1x3x1333x1333], format: [BxCxHxW], where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

Expected color order: BGR.


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 the Microsoft* COCO dataset version with 80 categories of objects.
  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, 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
  • 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])

Legal Information

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.