yolo-v1-tiny-tf

Use Case and High-Level Description

YOLO v1 Tiny is a real-time object detection model from TensorFlow.js* framework. This model was pre-trained on VOC dataset with 20 classes.

Conversion

  1. Install additional dependencies:

    h5py
    keras
    tensorflowjs
  2. Download model from here (tested on aa4354c commit).

  3. Convert model to Keras* format using tensorflowjs_converter script, e.g.:

    tensorflowjs_converter --input_format tfjs_layers_model --output_format keras <model_in>.json <model_out>.h5
  4. Convert the produced model to protobuf format.

    1. Get conversion script from repository :

      git clone https://github.com/amir-abdi/keras_to_tensorflow
    2. (Optional) Checkout the commit that the conversion was tested on:

      git checkout c841508a88faa5aa1ffc7a4947c3809ea4ec1228
    3. Apply keras_to_tensorflow.py.patch :

      git apply keras_to_tensorflow.py.patch
    4. Run script:

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

Specification

Metric

Value

Type

Detection

GFLOPs

6.988

MParams

15.858

Source framework

TensorFlow.js*

Accuracy

Accuracy metric obtained on test data from VOC2007 dataset for converted model.

Metric

Value

mAP

54.79%

Input

Original model

Image, name - input_1, shape - 1, 416, 416, 3, format is B, H, W, C, where:

  • B - batch size

  • H - height

  • W - width

  • C - channel

Channel order is RGB. Scale value - 255.

Converted model

Image, name - input_1, shape - 1, 416, 416, 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 detection summary info, name - conv2d_9/BiasAdd, shape - 1, 13, 13, 125, format is B, Cx, Cy, N\*25, where:

  • B - batch size

  • N - number of detection boxes for cell

  • Cx, Cy - cell index

Detection box has format [x, y, h, w, box_score, class_no_1, …, class_no_20], where:

  • (x, y) - raw coordinates of box center, apply sigmoid function to get coordinates relative to the cell

  • h, w - raw height and width of box, apply exponential function and multiply by corresponding anchors to get height and width values relative to cell

  • box_score - confidence of detection box, apply sigmoid function to get confidence in [0, 1] range

  • class_no_1, …, class_no_20 - probability distribution over the classes in logits format, apply softmax function and multiply by obtained confidence value to get confidence of each class

Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl.txt file.

The anchor values are 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52.

Converted model

The array of detection summary info, name - conv2d_9/BiasAdd/YoloRegion, shape - 1, 21125, which could be reshaped to 1, 125, 13, 13, format is B, N\*25, Cx, Cy, where:

  • B - batch size

  • N - number of detection boxes for cell

  • Cx, Cy - cell index

Detection box has format [x, y, h, w, box_score, class_no_1, …, class_no_20], where:

  • (x, y) - coordinates of box center relative to the cell

  • h, w - raw height and width of box, apply exponential function and multiply with corresponding anchors to get height and width values relative to the cell

  • box_score - confidence of detection box in [0, 1] range

  • class_no_1, …, class_no_20 - probability distribution over the classes in the [0, 1] range, multiply by confidence value to get confidence of each class

Mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl.txt file.

The anchor values are 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52.

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: