Preface: The article on Zhihu is very detailed about the use of trained models to generate annotation boxes, so let's make some usage records here.I found a source code written by Yann Henon on GitHub. I call it the initial version. The initial version has train.py. I used this version for initial learning and Faster-RCNN learning. The identification of pet dogs is also based on this project.However, it is very
Weka's classifiers are all placed in the packet inside that starts with weka.classifiers.According to their functions, they are divided into different categories. See their methods for details.The core class of Weka inside is placed in the package inside with weka.core as the beginning.
For Weka data, Instances inside exists.Then each piece of data is an instance of the interface Instrance (with and without s, which is well understood).
Nearest Neighbor Classification Algorithm The idea of KNN algorithm is summarized as follows: under the condition that the data and labels in the training set are known, test data are input, and the characteristics of the test data and the corresponding characteristics in the training set are compared with each other to find the first K data in the training set that are most similar to them, then the category
Multi-label Classification Problem
caffe corresponds to multiple label per sample?
training a multi-label classification/regression model using caffe
a single label image classification model using caffe fine-tune
Multi-label Detection of googlenet Based on caffe
Multi-label Training Based on Inception v3
Generate hdf5 File for Multi-label Training
caffehdf5layerdata > 2G import
caffe hdf5 data layer data generation
caffe learning notes (11): HDF5Data type dataset generation for multitasking learning.
This is a three-class problem adapted from cifar10: from __future__ import print_function #此为在老版本的python中兼顾新特性的一种方法 import keras from keras.preprocessing.image import ImageDataGenerator from keras.preprocessing import image from keras.models import Sequential from keras.models import model_from_json from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras
KNN DNN SVM DL BP DBN RBF CNN RNN ANN
This paper mainly introduces the commonly used neural networks, their main uses, and the advantages and limitations of various neural networks.
1 BP neural network BP (Back Propagation) neural network is a neural network learning algorithm.It is a hierarchical neural network composed of an input layer, an intermediate layer and an output layer, and the intermediate layer can be expanded into multiple layers.
Keras is a building block deep learning framework, which can be used to easily and intuitively build some common deep learning models.Before tensorflow came out, Keras was almost the most popular in-depth learning framework at that time, taking theano as the back end. Now Keras has supported four back ends at the same time: theano, tensorflow, cntk and mxnet (the first three official supports, mxnet has not yet been integrated
summary recently watching a Kaggle competition [toxcomment]
The goal of the competition is to judge whether a written comment is a poison comment or not.
At the same time, poison comments are specifically divided into six categories.【’toxic’, ‘severe_toxic’, ‘obscene’, ‘threat’, ‘insult’, ‘identity_hate’】
This blog mainly shares the new postures learned.
Keras can actually do multiple 2 classifications at the same time Baseline[0.051] implemented by Bi-LSTM actually does 6 2 classifications at the same time, but I didn't know that I could do this before!
Generally, we use accuracy when evaluating the performance of the classifier.
Consider in the context of multi-class classification
accuracy = (Number of Samples Correctly Classified)/(Number of Samples Classified)
In fact, it looks pretty good, but there may be a serious problem: for example, an opaque bag contains 1,000 mobile phones, including 600 iphone6, 300 galaxy s6, 50 Huawei mate7,50 and 50 mx4 (of course, these information classifiers are unknown.