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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 the original repository(tested on 47fdf189 commit)

  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. (Optional) Checkout the commit that the conversion was tested on: 
      git checkout c841508a88faa5aa1ffc7a4947c3809ea4ec1228
    3. Apply keras_to_tensorflow.patch: 
      git apply keras_to_tensorflow.patch
    4. Run script: 
      python keras_to_tensorflow.py --input_model=<model_in>.h5 --output_model=<model_out>.pb

Example

Specification

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

Accuracy

Metric Value
coco_precision 33.15%

Performance

Input

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.

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 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.