Constructs EfficientNetV2 model architectures as described in EfficientNetV2: Smaller Models and Faster Training.
model_efficientnet_v2_s(pretrained = FALSE, progress = TRUE, ...)
model_efficientnet_v2_m(pretrained = FALSE, progress = TRUE, ...)
model_efficientnet_v2_l(pretrained = FALSE, progress = TRUE, ...)
model_efficientnet_v2_s()
: EfficientNetV2-S model
model_efficientnet_v2_m()
: EfficientNetV2-M model
model_efficientnet_v2_l()
: EfficientNetV2-L model
Image classification with 1000 output classes by default (ImageNet).
The models expect input tensors of shape (batch_size, 3, H, W)
.
Typical values for H
and W
are 384 for V2-S, 480 for V2-M,
and 512 for V2-L.
Model | Resolution | Params (M) | GFLOPs | Top-1 Acc. |
V2-S | 384 | 24 | 8.4 | 83.9 |
V2-M | 480 | 55 | 24 | 85.1 |
V2-L | 512 | 119 | 55 | 85.7 |
Other models:
model_alexnet()
,
model_efficientnet
,
model_inception_v3()
,
model_mobilenet_v2()
,
model_resnet
,
model_vgg