The levit-128s
model is one of the LeViT models family: a hybrid neural network for fast inference image classification. The model is pre-trained on the ImageNet dataset. LeViT-128s model is a small LeViT variant that has 128 channels on input of the transformer stage and 2, 3 and 4 number of pairs of Attention and MLP blocks at 1, 2 and 3 model stages respectively.
The model input is a blob that consists of a single image of 1, 3, 224, 224
in RGB
order.
The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.
For details see repository and paper.
Metric | Value |
---|---|
Type | Classification |
GFLOPs | 0.6177 |
MParams | 8.2199 |
Source framework | PyTorch* |
Metric | Value |
---|---|
Top 1 | 76.54% |
Top 5 | 92.85% |
Image, name - image
, shape - 1, 3, 224, 224
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is RGB
.
Mean values - [123.675,116.28,103.53], scale values - [58.395, 57.12, 57.375].
Image, name - image
, shape - 1, 3, 224, 224
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
Object classifier according to ImageNet classes, name - probs
, shape - 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in logits format
Object classifier according to ImageNet classes, name - probs
, shape - 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in logits 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>
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
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-PyTorch-Image-Models.txt
.