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smartlab-action-recognition-0001 (composite)

Use Case and High-Level Description

There are 3 models for smartlab action recogntion including two encoder models and one decoder model.

These models are fine-tuned with smartlab dataset to predict actions and can classfy 3 types of action including "noise_action", "put_take" and "adjust_rider".

Example of the input data

Example of the output

Output put_take action

Composite model specification

Metric Value
Accuracy on the DSI1867 TODO
Source framework PyTorch*

Encoder models specification

The smartlab-action-recognition-0001-encoder-* have Mobilenet-V2 like backbone with convolutional encoder part of the action recognition.

There are two models called: smartlab-action-recognition-0001-encoder-side and smartlab-action-recognition-0001-encoder-top, which have the same strcuture but different weights.

Metric Value
GFlops 0.611
MParams 3.387

Inputs

Image, name: input_image, shape: 1, 3, 224, 224 in the B, C, H, W format, where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width Expected color order is BGR

Outputs

  1. Name: output_feature, shape: 1, 1280. Features from encoder part of action recogntion head.

Decoder model specification

The smartlab-action-recognition-0001-decoder is a fully connected decoder part which accepts features from top and front views, computed by encoder and predicts score for action across following label list: no_action, noise_action, adjust_rider

Metric Value
GFlops 0.008
MParams 4.099

Inputs

  1. Name: input_feature_1, shape: 1, 1280. Encoded features from topview.
  2. Name: input_feature_2, shape: 1, 1280. Encoded features from frontview.

Outputs

  1. Name: decoder_hidden, shape: 1, 3. The foramt [has_action_conf_score, action_1_logits, action_2_logits]
    • has_action_conf_score - confidence for action frame. If>0.5, there is specified action.
    • action_1_logits - confidence for the put_take action class
    • action_2_logits - confidence for the adjust_rider action class

Classification confidence scores in the [0, 1] range.

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

Legal Information

[*] Other names and brands may be claimed as the property of others.