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¶

Clone or download original repository:

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

(Optional) Checkout the commit that the conversion was tested on:
```
git checkout 0667e88
```

Install prerequisites, tested with:

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

Copy <omz_dir>/models/public/i3d-rgb-tf/freeze.py script 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%	64.83%
Top 5	86.01%	84.58%

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, 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.

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 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:

Action Recognition Python\* Demo

Legal Information¶

The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in <omz_dir>/models/public/licenses/APACHE-2.0.txt.