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*

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 [0, 1] range

### Converted model

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

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.