![]() Resolution: Resolutions not divisible by 8, 16, etc.However, choice of resolution,depth and width are also restricted by many factors: (This section provides some details on "compound scaling", and can be skippedif you're only interested in using the models)īased on the original paper people may have theimpression that EfficientNet is a continuous family of models created by arbitrarilychoosing scaling factor in as Eq.(3) of the paper. These extensions of the model can be usedby updating weights without changing model architecture. Such a scalingheuristics (compound-scaling, details see Tan and Le, 2019) allows theefficiency-oriented base model (B0) to surpass models at every scale, while avoidingextensive grid-search of hyperparameters.Ī summary of the latest updates on the model is available at here, where variousaugmentation schemes and semi-supervised learning approaches are applied to furtherimprove the imagenet performance of the models. By introducing a heuristic way toscale the model, EfficientNet provides a family of models (B0 to B7) that represents agood combination of efficiency and accuracy on a variety of scales. The smallest base model is similar to MnasNet, whichreached near-SOTA with a significantly smaller model. requiring least FLOPS for inference)that reaches State-of-the-Art accuracy on bothimagenet and common image classification transfer learning tasks. GitHub source Introduction: what is EfficientNetĮfficientNet, first introduced in Tan and Le, 2019is among the most efficient models (i.e.Return Value: It returns tf.Tensor4D object.Įxample 1: Using a 4d tensor, boxes, boxInd, and cropSize parameters.Description: Use EfficientNet with weights pre-trained on imagenet for Stanford Dogs classification. extrapolationValue: It is the stated threshold that is used to conclude at which time to delete boxes on the basis of the stated score.It can be of type ‘bilinear’, or ‘nearest’. method: It is an optional parameter that defines the sampling method for rescaling.cropSize: It is the stated 1d int32 tensor that has two elements and is of configuration defining the length to which each and every crops are rescaled to. ![]() It is of type tf.Tensor1D, TypedArray, or Array. boxInd: The stated 1d int32 tensor, which is of configuration along with values in the range [0, batch) which defines the image that the i-th box indicates.It can be of type tf.Tensor2D, TypedArray, or Array. And every access is, allowing that (y1, x1) and (y2, x2) are the standardized coordinates of the box in the boxInd image in the group. boxes: The stated 2d float32 tensor, which is of configuration.It can be of type tf.Tensor4D, TypedArray, or Array. Where, imageHeight as well as imageWidth should be positive, defining the group of images from which the crops are to be taken. images: The stated 4d tensor, which is of configuration.Parameters: This method accepts the following parameters: Syntax: tf.image.cropAndResize(image, boxes, boxInd, cropSize, image.cropAndResize() function is used to take out the outputs from the stated input image tensor as well as rescales them through bilinear sampling or else nearest neighbor sampling to a normal output dimension as stated by crop length. ![]()
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