Image classification with NVIDIA TensorRT from TensorFlow models. - NVIDIA-AI-IOT/tf_to_trt_image_classification
a TensorFlow Lite classifier model that identifies what is in the image. Up to three results with the highest confidence returned by the classifier are shown on the screen, if there is an attached display. Also, the result is spoken out loud using Text-To-Speech to the default audio output...
TensorFlowis a well established, open source machine learning and deep learning framework that can be used to create and run a wide range of different models, usually using powerful machines in the cloud. In addition, TensorFlow also supports running models on mobile devices through theTensorFlow.M...
TF-slim is a new lightweight high-level API of TensorFlow (tensorflow.contrib.slim) for defining, training and evaluating complex models. This directory contains code for training and evaluating several widely used Convolutional Neural Network (CNN) image classification models using TF-slim. It conta...
git clone https://github.com/Azure/azureml-examples.git Understand the code The code for this tutorial uses TensorFlow to train an image classification machine learning model that categorizes handwritten digits from 0-9. It does so by creating a neural network that takes the pixel values of...
=tf.image.random_flip_left_right(distorted_image)#Because these operations are not commutative, consider randomizing#the order their operation.#NOTE: since per_image_standardization zeros the mean and makes#the stddev unit, this likely has no effect see tensorflow#1458.distorted_image =tf.image....
Using a simulated optical convolution with this phase mask, the classification model achieves a test accuracy of 70.1%, comparable to the accuracy of digital convolution with the target tiled kernels. These results suggest that the opt-conv layer is able to learn behavior similar to a digital ...
To train the discriminator, first the generator generates an output image. The discriminator looks at the input/target pair and the input/output pair and produces its guess about how realistic they look. The weights of the discriminator are then adjusted based on the classification error of the ...
Using TensorFlow backend. ImageDataGenerator for Single image ImageDataGenerator 参数很多,详见这里或者在python环境下输入ImageDataGenerator?,我们先看一个例子,这个例子将对一张图片进行数据扩充 # 指定参数 # rotation_range 旋转 # width_shift_range 左右平移 ...
(lines, corners, shapes, textures) immediately apply to any dataset. Since image segmentation does classification at the pixel level it makes sense to transfer learning from image classification datasets such as ILSVRC2012. This turns out to be rather straightforward when using Caffe—with one or ...