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.

Metric

Value

Type

Classification

GFLOPs

10.8148

MParams

27.4493

Source framework

PyTorch*

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 size

• C - channel

• H - height

• W - 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 size

• C - channel

• H - height

• W - 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 size

• C - predicted probabilities for each class in logits format

Converted model¶

The converted model has the same parameters as the original model.

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.

omz_downloader --name <model_name>
omz_converter --name <model_name>