ResNeSt-50 is image classification model pretrained 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 "1x3x224x224" 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% |
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].
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.
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 [0, 1] range
The converted model has the same parameters as the original model.
The original model is distributed under the Apache License, Version 2.0. A copy of the license is provided in APACHE-2.0.txt.
[*] Other names and brands may be claimed as the property of others.