resnet | 易学教程

笔记七.CVPR目标检测论文阅读ScratchDet: Training Single-Shot Object Detectors from Scratch

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一.背景问题目前在数据集ImageNet上预训练现成网络，再进行微调，存在的问题： 1）分类和检测任务对目标位置有不同程度的敏感度，导致最终学习目标产生偏差； 2）该体系结构受分类网络的限制，给修改带来不便。为了解决这些问题，从零开始训练探测器是一个可行的解决方案。通过大量的实验和对下采样因子的分析，提出了一种充分利用原始图像信息的root-ResNet骨干网。我们的ScratchDet在所有从头开始的训练检测器中，在PASCAL VOC 2007、2012和MS COCO上都达到了最先进的精度，甚至比几种一级预处理方法性能更好。代码：https://github.com/kimsoyone/ScratchDet。二.论文解决因此，要想从零开始利用训练检测器，需要实现两个方面的改进：（1）在保证训练收敛性的同时，不受任何类型网络的体系结构限制；（2）使训练检测器的性能与预训练网络一样好（甚至更好）。本文的主要贡献概括如下：（1）我们提出了一种从零开始训练的单阶段目标检测器ScratchDet，它集成了BatchNorm以帮助检测器从零开始很好地收敛，独立于网络类型。（2）提出了一种新的root-ResNet骨干网，显著提高了检测精度，特别是对小目标的检测。（3） ScratchDet的性能优于最先进的从头开始的训练检测器和一些基于预训练的检测器。三

经典分类模型(七)：ResNext（2017）

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Aggregated Residual Transformations for Deep Neural Networks----2017ResNext Abstract 我们提出了一种用于图像分类的简单，高度模块化的网络体系结构。我们的网络是通过重复构建模块来构建的，该模块聚合具有相同拓扑的一组转换。我们的简单设计导致了同类的多分支架构，仅需设置几个超参数。此策略提供了一个新维度，我们将其称为“基数”（转换集的大小），它是深度和宽度维度之外的一个重要因素。在ImageNet-1K数据集上，我们根据经验表明，即使在保持复杂性的限制条件下，增加基数也可以提高分类精度。此外，当我们增加容量时，增加基数比深入或更广泛更有效。我们的模型名为 ResNeXt ，是我们进入2016年ILSVRC分类任务的基础，我们获得了第二名。我们进一步在ImageNet-5K集和COCO检测集上对ResNeXt进行了研究，其结果也比ResNet同类要好。该代码和模型可以在线公开获得1。 1.Introduction 视觉识别的研究正在经历从“功能工程”到“网络工程”的转变[25、24、44、34、36、38、14]。与传统的手工设计特征（例如，SIFT [29]和HOG [5]）相反，神经网络从大规模数据中学习的特征[33]在训练过程中所需的人力最少，并且可以转移到各种识别任务中[7，10

图像分类论文列表最全集合！步入计算机视觉领域！

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Image Classification Papers Background I believe image classification is a great start point before diving into other computer vision fields, espacially for begginers who know nothing about deep learning. When I started to learn computer vision, I’ve made a lot of mistakes, I wish someone could have told me that which paper I should start with back then. There doesn’t seem to have a repository to have a list of image classification papers like deep_learning_object_detection until now. Therefore, I decided to make a repository of a list of deep learning image classification papers and codes to

Can't reload a trained resnet50 model from a h5 file

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问题 I am very new on neural network, and I am trying to use Keras to build a fish classifier. I got a python code that uses Keras with tensorflow-backend to create a RESNET-50 model, the code use model.save() to output a h5 file. I write another python file to reload the RESNET-50 model from the h5 file. I use keras.models.load_model() to load h5 file. But python give me the error message like below, and I don't knwo why. Please give me a hand, thanks. Traceback (most recent call last): File

Modify layers in resnet model

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问题 I am trying to train resnet50 model for image classification problem. I have loaded the pretrained 'imagenet' weights before training the model on the dataset I have. I want to insert a layer (mean subtraction layer) in-between the input layer and the first convolutiuon layer. model = ResNet50(weights='imagenet') def mean_subtract(img): img = T.set_subtensor(img[:,0,:,:],img[:,0,:,:] - 123.68) img = T.set_subtensor(img[:,1,:,:],img[:,1,:,:] - 116.779) img = T.set_subtensor(img[:,2,:,:],img[:

Keras pretrain CNN with TimeDistributed

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问题 Here is my problem, I want to use one of the pretrain CNN network in a TimeDistributed layer. But I have some problem to implement it. Here is my model: def bnn_model(max_len): # sequence length and resnet input size x = Input(shape=(maxlen, 224, 224, 3)) base_model = ResNet50.ResNet50(weights='imagenet', include_top=False) for layer in base_model.layers: layer.trainable = False som = TimeDistributed(base_model)(x) #the ouput of the model is [1, 1, 2048], need to squeeze som = Lambda(lambda x

preprocessing images generated using keras function ImageDataGenerator() to train resnet50 model

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问题 I am trying to train resnet50 model for image classification problem.I have loaded the 'imagenet' pretrained weights before training the model on the image dataset I have. I am using keras function flow_from_directory() to load images from directory. train_datagen = ImageDataGenerator() train_generator = train_datagen.flow_from_directory( './train_qcut_2_classes', batch_size=batch_size, shuffle=True, target_size=input_size[1:], class_mode='categorical') test_datagen = ImageDataGenerator()

TensorFlow/Keras - expected global_average_pooling2d_1_input to have shape (1, 1, 2048) but got array with shape (7, 7, 2048)

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问题 I'm fairly new to TensorFlow and Image Classification, so I may be missing key knowledge and is probably why I'm facing this issue. I've built a ResNet50 model in TensorFlow for the purpose of image classification of Dog Breeds using the ImageNet library and I have successfully trained a neural network which can detect various Dog Breeds. I'm now at the point in which I would like to pass a random image of a dog to my model for it to spit out an output on what it thinks the dog breed is.

Tensorflow Lite: ResNet example model gave VERY poor result during validation with ImageNet

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问题 I am studying tensorflow lite. I downloaded the ResNet frozen graph ResNet_V2_101 from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/models.md#image-classification-float-models . And then I followed https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tutorials/post_training_quant.ipynb to convert this frozen graph to both Lite model and quantized lite model. import tensorflow as tf import pathlib import sys import tensorflow as tf from tensorflow

经典 network -- 图像分类篇（03 ResNet v1-v2）

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近期，实验室小组成员决定定期学习经典网络模型。因此，特别准备写这么一个博客，持续更新我们的学习、及个人对各种经典网络的理解。如有不足和理解不到位的地方，还望读者提出质疑和批评，定虚心改进。望共同讨论、学习和进步。系列目录：经典 network -- 图像分类篇（01 AlexNet / VGG）经典 network -- 图像分类篇（02 Inception v1-v4）(-ing) 经典 network -- 图像分类篇（03 ResNet v1-v2）经典 network -- 图像分类篇（03 ResNet v1-v2）本部分包括 ResNet，ResNet v2，ResNeXt。 ResNet [paper] Deep Residual Learning for Image Recognition [github] https://github.com/KaimingHe/deep-residual-networks [pytorch] https://pytorch.org/hub/pytorch_vision_resnet/ Introduction We explicitly reformulate the layers as learning residual functions with reference to the layer inputs,

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