Image classification with NVIDIA TensorRT from TensorFlow models. - NVIDIA-AI-IOT/tf_to_trt_image_classification
When a button is pushed or when the touchscreen is touched, the current image is captured from the camera. The image is then converted and piped into a TensorFlow Lite classifier model that identifies what is in the image. Up to three results with the highest confidence returned by the clas...
TF-slim是用于定义,训练和评估复杂模型的TensorFlow(tensorflow.contrib.slim)的新型轻量级高级API。 该目录包含用于训练和评估使用TF-slim的几种广泛使用的卷积神经网络(CNN)图像分类模型的代码。 它包含脚本,允许您从头开始训练模型或从预训练的网络权重微调它们。 它还包含用于下载标准图像数据集的代码,将其转换为Tenso...
4. 训练模型代码 1importnumpy as np2importos3importglob4importmath5fromos.pathimportjoin as join_path6importjoblib7fromtensorflow.kerasimportbackend as K8fromtensorflow.keras.callbacksimportEarlyStopping, ModelCheckpoint9fromtensorflow.keras.preprocessing.imageimportImageDataGenerator10fromtensorflow.keras.optimize...
Nathan Silberman, github:nathansilberman Sergio Guadarrama, github:sguada Citation "TensorFlow-Slim image classification model library" N. Silberman and S. Guadarrama, 2016.https:///tensorflow/models/tree/master/research/slim Table of contents
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...
ImageNet Classification with Deep Convolutional Neural Networks. 2012PDF 参考:https://my.oschina.net/u/876354/blog/1633143 ImageNet Top1错误率:37.5%,而两年前非深度学习的方法的最好错误率是45.7% ImageNet Top5错误率:17.0%,而两年前非深度学习的方法的最好错误率是25.7% ...
论文1:Self-training with Noisy Student improves ImageNet classification 机器之心 2019/11/18 7970 论文推荐:EfficientNetV2 - 通过NAS、Scaling和Fused-MBConv获得更小的模型和更快的训练 linux EfficientNetV2是由 Google Research,Brain Team发布在2021 ICML的一篇论文,它结合使用NAS和缩放,优化训练速度和参数效率...
We simulated our models in a TensorFlow framework, using built-in functions whenever possible. Since some of the convolutional kernels and PSFs we tried to optimize were much larger than those of a standard CNN, we used an FFT-based convolution to increase computation speed when possible. All ...
这部分的实例请见我之前写的文章猫,青蛙,老鼠的例子:引入TensorFlow3——交叉熵作为loss函数。 五、CNN网络——AlexNet • 5个卷积层 + 3个全连接层 • 60M个参数 + 650K个神经元 • 2个分组 2个GPU(3GB) • 训练时长一周, 50x加速 ...