resnest-50-pytorch#
Use Case and High-Level Description#
ResNeSt-50 is image classification model pre-trained on ImageNet dataset. ResNeSt is stacked in ResNet-style from modular Split-Attention blocks that enables attention across feature-map groups.
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.
Specification#
Metric |
Value |
---|---|
Type |
Classification |
GFLOPs |
10.8148 |
MParams |
27.4493 |
Source framework |
PyTorch* |
Accuracy#
Metric |
Value |
---|---|
Top 1 |
81.11% |
Top 5 |
95.36% |
Input#
Original model#
Image, name - data
, 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].
Converted model#
Image, name - data
, shape - 1, 3, 224, 224
, format is B, C, H, W
, where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
Output#
Original model#
Object classifier according to ImageNet classes, name - prob
, shape - 1, 1000
, output data format is B, C
, where:
B
- batch sizeC
- predicted probabilities for each class in logits format
Converted model#
The converted model has the same parameters as the original model.
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:
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
.
[*] Other names and brands may be claimed as the property of others.