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i3d-rgb-tf

Use Case and High-Level Description

The i3d-rgb-tf is a model for video classification, based on paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset”. This model use RGB input stream and trained on Kinetics-400 dataset. Additionally, this model has initialize values from Inception v1 model pre-trained on ImageNet dataset.

Originally redistributed as a checkpoint file, was converted to frozen graph.

Conversion

  1. Clone or download original repository:

    git clone https://github.com/deepmind/kinetics-i3d.git

  2. (Optional) Checkout the commit that the conversion was tested on:

    git checkout 0667e88

  3. Install prerequisites, tested with:

    tensorflow==1.11
tensorflow-probability==0.4.0
dm-sonnet==1.26

  4. Copy <omz_dir>/models/public/i3d-rgb-tf/freeze.py to root directory of original repository and run it:

    python freeze.py

Specification

Metric

Value

Type

Action recognition

GFLOPs

278.981

MParams

12.69

Source framework

TensorFlow*

Accuracy

Accuracy validations performed on validation part of Kinetics-400 dataset. Subset consists of 400 randomly chosen videos from this dataset.

Metric

Converted Model

Converted Model (subset 400)

Top 1

65.96%

67.0%

Top 5

86.01%

88.7%

Input

Original Model

Video clip, name - Placeholder, shape - 1, 79, 224, 224, 3, format is B, D, H, W, C, where:

  • B - batch size

  • D - duration of input clip

  • H - height

  • W - width

  • C - channel

Channel order is RGB. Mean value - 127.5, scale value - 127.5.

Converted Model

Video clip, name - Placeholder, shape - 1, 79, 3, 224, 224, format is B, D, C, H, W, where:

  • B - batch size

  • D - duration of input clip

  • C - channel

  • H - height

  • W - width

Channel order is RGB.

Output

Original Model

Action classifier according to Kinetics-400 action classes, name - Softmax, shape - 1, 400, format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

Converted Model

Action classifier according to Kinetics-400 action classes, name - Softmax, shape - 1, 400, format is B, C, where:

  • B - batch size

  • C - predicted probabilities for each class in [0, 1] range

Download a Model and Convert it into Inference Engine Format

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

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>
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