Training Techniques | RMSProp, Weight Decay, Label Smoothing |
---|---|
Architecture | Average Pooling, Dropout, Inception-ResNet-v2-A, Inception-ResNet-v2-B, Inception-ResNet-v2-C, Inception-ResNet-v2 Reduction-B, Reduction-A, Softmax |
ID | inception_resnet_v2 |
SHOW MORE |
Inception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates residual connections (replacing the filter concatenation stage of the Inception architecture).
To load a pretrained model:
import timm
m = timm.create_model('inception_resnet_v2', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. inception_resnet_v2
. You can find the IDs in the model summaries at the top of this page.
You can follow the timm recipe scripts for training a new model afresh.
@misc{szegedy2016inceptionv4,
title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
year={2016},
eprint={1602.07261},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
ImageNet | inception_resnet_v2 | Top 1 Accuracy | 0.95% | # 330 |
Top 5 Accuracy | 17.29% | # 330 |