EfficientNetLiteB0Backbone in KerasHub

[cjohn001]

Hello together,
I am currently trying to migrate my code to KerasHub, as it seems for KerasCV the documentation is gone now. In the past I was using the EfficientNetLiteB0Backbone. Seems like it has not been ported to KerasHub. Is there a chance to see this model in KerasHub as well? As I am not seeing it yet I am wondering, is there nowadays maybe a better model to use? I need something small with good classification results for a mobile phone, I think EfficientNetLite was doing the job best with KerasCV.

And in case it will be moved to KerasHub, it would also be great if one could get a pretrained weights for it. Thanks for consideration.

[edge7]

Hi, am not part of the Keras team, so this is my personal opinion.

I understand your issue, though. As Keras Hub is still WIP, the Keras CV documentation should still be around. I got a similar issue for an other model recently.
EfficientNetLiteB0 is not ported yet, it probably will.
You can still use Keras CV for the time being, look here in particular at the tests, you might be able to load it correctly even without official documentation.

[sachinprasadhs]

Hi, We have a EfficientNet Lite model for the edge devices here https://www.kaggle.com/models/keras/efficientnet/keras/efficientnet_lite0_ra_imagenet which has been ported from Timm.
For more details about the model check https://huggingface.co/timm/efficientnet_lite0.ra_in1k

[cjohn001]

Hello, thanks for the directions. For the moment this code works for me, I am primary missing pretrained weights and the docs. If it get ported all is fine. For the moment I can live without the docs. Some kind of versioning in the docs would be great.

efnetlite_backbone = keras_cv.models.EfficientNetLiteB0Backbone(include_rescaling=True,
                                                                input_shape=(HEIGHT,WIDTH,3))
efnetlite_classifier = keras_cv.models.ImageClassifier(efnetlite_backbone, num_classes=len(class_mapping), activation='sigmoid')
efnetlite_classifier.compile(loss=keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
                   optimizer=keras.optimizers.SGD(momentum=0.9),
                   metrics=["accuracy"])