Use EfficientNetB0 for classifying 1000 classes of images from imagenet, run:

Instead of allowing arbitray choice of width / depth / resolution parameters.Īn implementation of EfficientNet B0 to B7 has been shipped with tf.keras since TF2.3. Therefore, the keras implementation (detailed below) only provide these 8 models, B0 to B7, Width but keep resolution can still improve performance.Īs a result, the depth, width and resolution of each variant of the EfficientNet modelsĪre hand-picked and proven to produce good results, though they may be significantly In such a situation, increasing depth and/or

Resource limit: Memory limitation may bottleneck resolution when depthĪnd width can still increase.

Depth and width: The building blocks of EfficientNet demands channel size to be.

Variants of the model, hence the input resolution for B0 and B1 are chosen as 224 and Of some layers which wastes computational resources. Resolution: Resolutions not divisible by 8, 16, etc.However, choice of resolution,ĭepth and width are also restricted by many factors: Impression that EfficientNet is a continuous family of models created by arbitrarilyĬhoosing scaling factor in as Eq.(3) of the paper. If you're only interested in using the models)īased on the original paper people may have the (This section provides some details on 'compound scaling', and can be skipped These extensions of the model can be usedīy updating weights without changing model architecture.

Improve the imagenet performance of the models. Heuristics (compound-scaling, details seeĮfficiency-oriented base model (B0) to surpass models at every scale, while avoidingĮxtensive grid-search of hyperparameters.Ī summary of the latest updates on the model is available atĪugmentation schemes and semi-supervised learning approaches are applied to further Good combination of efficiency and accuracy on a variety of scales. Scale the model, EfficientNet provides a family of models (B0 to B7) that represents a Reached near-SOTA with a significantly smaller model. The smallest base model is similar to MnasNet, which Imagenet and common image classification transfer learning tasks. That reaches State-of-the-Art accuracy on both Image classification via fine-tuning with EfficientNetĭescription: Use EfficientNet with weights pre-trained on imagenet for Stanford Dogs classification.ĮfficientNet, first introduced in Tan and Le, 2019

Tuning steps with binary editor