Feature vectors of images with ResNet V1 50 trained on ImageNet (ILSVRC-2012-CLS).
ResNet (later renamed ResNet V1) is a family of network architectures for image classification with a variable number of layers, originally published by
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: "Deep Residual Learning for Image Recognition", 2015.
This TF-Hub module uses the TF-Slim implementation of
with 50 layers.
The module contains a trained instance of the network, packaged to get
feature vectors from images.
If you want the full model including the classification it was originally
trained for, use module
The weights for this module were obtained by training on the ILSVRC-2012-CLS dataset for image classification ("Imagenet") with TF-Slim's "Inception-style" preprocessing.
This module implements the common signature for computing image feature vectors. It can be used like
module = hub.Module("https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/1") height, width = hub.get_expected_image_size(module) images = ... # A batch of images with shape [batch_size, height, width, 3]. features = module(images) # Features with shape [batch_size, num_features].
...or using the signature name
image_feature_vector. The output for each image
in the batch is a feature vector of size
num_features = 2048.
For this module, the size of the input image is fixed to
width = 224 x 224 pixels.
images are expected to have color values in the range [0,1],
common image input
Consumers of this module can fine-tune it.
This requires importing the graph version with tag set
in order to operate batch normalization in training mode.