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- visual C++ 编程实现图片分类处理算法
pcasearch
- 基于焊接图片的pca降维，knn分类算法。-Pca-based solder image dimension reduction, knn classification algorithm.
svm
- SVM分类器 分类各种图片的类别 分类各种图片的类别 -SVM classifiers various pictures of various categories of classification of classified images of various image types
htl4ic
- 使用新兴的机器学习方法：迁移学习进行图片分类，分类效果明显提高。-Use the emerging machine learning methods: migration study carried pictures segments, segment results improved significantly.
image-sentiment-analysis
- 图片情感分析模型，基于卷积神经网络，以颜色特征为依据进行情感分类，图片情感极性分为积极和消极两类。(The model can extract the hue, brightness, contrast and other information from a picture to represent the emotional polarity of the image. The image sentiment analysis model is using convolution neura
BP网络图像分类
- 识别图像中道路，建筑，基于BP神经网络，里面有图片(Identify roads, buildings, etc in images)
GoogleNet_MATLAB-master
- GoogleNet 卷积神经网络 图片分类 分类精度高 网络结构深(GoogleNet convolution neural network image classification, high classification accuracy, network structure is deep)
label_image
- 基于TensorFlow框架，实现对图片分类(Achieve classification of pictures)
knn
- 人工智能导论课作业，水杯图片的分类，knn方法实现(Homework of AI. classify images of cups and bottles. Using knn)
mlp
- 人工智能导论作业，用mlp方法实现的水杯图片分类，生成loss的下降趋势图(Homework of AI. Using MLP to classify images of cups and bottles, and plot loss of the training process.)
svm
- 人工智能导论作业，用SVM方法实现的水杯图片分类，并生成loss的下降趋势图(Homework of AI. Using SVM to classify images of cups and bottles.)
softmax
- 人工智能导论作业，用softmax方法实现的水杯图片分类，可扩展到其他分类任务(Homework of AI. Classify images of cups using softmax. Can be used in other tasks.)
svmtrain
- 基于支持向量机的对指定多个包含特征的训练集图片，包含label信息。训练后，可对于相同格式的图片进行分类。(A training set image containing multiple features is included in the support vector machine (SVM), which contains label information. After training, the pictures in the same format can be classifi
tfAlexNet-master
- 基于tensorflow的alexnet实现，用于机器学习图片分类网络模型入门(Tensorflow based alexnet implementation for machine learning picture classification network model introduction)
matlab实现LeNet
- 卷积神经网络LeNet代码，可实现图片分类(Convolution neural network code)
cifar10_tutorial
- 非常适合入门的一个深度学习图片分类例程！(Very suitable for beginners to learn a deep picture classification routines!)
SVM做图片处理
- 使用SVM算法对CIFAR-10图片数据集进行分类，包括模型的训练，测试和参数的调优(Using SVM algorithm to classify CIFAR-10 image data sets, including model training, testing and parameter tuning)
CNN
- 通过卷积网络，自动实现对图片特征的提取，通过训练，得到有效的权值，进行图像分类(Through convolution network, automatic extraction of image features can be realized. Through training, effective weights can be obtained and image classification can be carried out.)
symbol_resnet
- RACNN注意力机制，细腻度图片分类。 RA-CNN由上到下用了3个尺度并且越来越精细，尺度间构成循环，即上层的输出作为当层的输入。RA-CNN主要包含两部分：每一个尺度上的卷积网络和相邻尺度间的注意力提取网络(APN, Attention Proposal Network)。在每一个尺度中，使用了堆叠的卷积层等，最后接上全连接层于softmax层，输出每一个类别的概率；这个是很好理解的，代码采用的网络结构是VGG的网络结构。(RACNN attention mechanism)
04.CNN处理CiFar
- 以python语言为基础，利用tensorflow机器学习架构，两层卷积神经网络实现，CiFar数据集图片分类功能。(Based on Python language, using tensorflow machine learning architecture, two-layer convolutional neural network, CiFar data set image classification function.)