While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional ...
Bag of Tricks for Image Classification with Convolutional Neural Networks，trick 合集 1。Must Know T...
Paper:https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.pdf 本文提出了一种 model 多标签之间关系的一种模型,即:CNN-LSTM 模型。 我认为该模型的想法来自于 Image Caption的常规套路。 上图就是本文的流程图,可以看到,类似 Image Caption的思路,本...
2012年,在计算机视觉“世界杯”之称的ImageNet图像分类竞赛中,Geoffery E.Hinton等人凭借卷积神经网络Alex-Net[2]以超过第二名近12%的准确率一举夺得该竞赛冠军,霎时间学界业界纷纷惊愕哗然。自此边揭开了卷积神经网络在计算机视觉领域逐渐称霸的序幕,此后每年的ImageNet竞赛的冠军非卷积神经网络莫属。直到2015年,卷积神...
In this study, we proposed a CNN-LSTM-att hybrid model for classification of growth status of Chinese fir seedlings under drought and heat stress, respectively. Two RGB image datasets of Chinese fir seedling under drought and heat stress were generated for the first time, and utilized in this...
the semantic feature extraction part and the classification part. Since the dimensionality of HSIs can be reduced by the spectral feature refining part, the dimensionality reduction operation for HSIs becomes an embedded component of the CNN model. The advantages of this embedded dimensionality reductio...
Image augmentation was employed to prevent overfitting. The deep neural network model performed the best, achieving a 97% AUC, 93.28% sensitivity, and 91.38% specificity [25]. Ibrahim et al. developed three artificial intelligence techniques for the early diagnosis of autism: neural networks with ...
model architecture: CNN-non-static using: word2vec vectors [('image shape', 64, 300), ('filter shape', [(100, 1, 3, 300), (100, 1, 4, 300), (100, 1, 5, 300)]), ('hidden_units', [100, 2]), ('dropout', [0.5]), ('batch_size', 50), ('non_static', True), (...
model architecture: CNN-non-static using: word2vec vectors [('image shape', 64, 300), ('filter shape', [(100, 1, 3, 300), (100, 1, 4, 300), (100, 1, 5, 300)]), ('hidden_units', [100, 2]), ('dropout', [0.5]), ('batch_size', 50), ('non_static', True), (...
经过ImageNet训练的模型本身已经学会了更通用的特征表示(universal feature representation),特别是低/中端的特征(比如轮廓,纹理的) 因此我们能通过使用预训练模型(Pretrained model),能让我们在仅有几百张训练图片的情况下,让模型取得较好的效果 具体可以看我的文章: 更多关于迁移学习的资料,可以移步: 网上已经有很多...