a7d2bbaaa8
* added efficientnet image preprocessor but tests fail * ruff checks pass * ruff formatted * properly pass rescale_offset through the functions * - corrected indentation, ordering of methods - reshape test passes when casted to float64 - equivalence test doesn't pass * all tests now pass - changes order of rescale, normalize acc to slow - rescale_offset defaults to False acc to slow - resample was causing difference in fast and slow. Changing test to bilinear resolves this difference * ruff reformat * F.InterpolationMode.NEAREST_EXACT gives TypeError: Object of type InterpolationMode is not JSON serializable * fixes offset not being applied when do_rescale and do_normalization are both true * - using nearest_exact sampling - added tests for rescale + normalize * resolving reviews --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
3.1 KiB
EfficientNet
Overview
The EfficientNet model was proposed in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models.
The abstract from the paper is the following:
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.
This model was contributed by adirik. The original code can be found here.
EfficientNetConfig
autodoc EfficientNetConfig
EfficientNetImageProcessor
autodoc EfficientNetImageProcessor - preprocess
EfficientNetImageProcessorFast
autodoc EfficientNetImageProcessorFast - preprocess
EfficientNetModel
autodoc EfficientNetModel - forward
EfficientNetForImageClassification
autodoc EfficientNetForImageClassification - forward