This research introduces ScaledDenseNet, a proficient deep learning architecture developed for precise identification of skin lesions. The model amalgamates DenseNet with the compound scaling method derived from EfficientNet, thereby achieving enhanced performance without detriment to the speed of...
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In this paper, we propose an architecture that distills this insight into a simple connectivity pattern: to ensure maximum information flow between layers in the network, we connect all layers (with matching feature-map sizes) directly with each other. 图1有这种思想的图示(当成图示看吧,解释论文...
Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for ...
Wojna. Rethinking the inception architecture for computer vision. In CVPR, 2016. 2, 3, 4 对于DenseNet特别有效,具体在该网络中为BN-ReLU-Conv( 1\times1 )-BN-ReLU-Conv( 3\times3 ),其中BN是批归一化。在实验中设置每个 1\times1 卷积产生 4k 个特征图。 压缩(Compression)技术 作者为了进一步提高...
It can detect COVID-19 from CT Scan Images using CNN based on DenseNet121 architecture. heroku django deep-learning reactjs tensorflow django-rest-framework convolutional-neural-network densenet121 ct-scan covid-19 covid19 covid-19-detection Updated Sep 6, 2023 JavaScript hugohiraoka / ...
X-ray imaging;DenseNet architecture;convolutional neural networks (CNNs) 1. Introduction Pneumonia, an acute respiratory infection characterized by the inflammation of the alveoli in the lungs [1], remains a significant global health concern [2], particularly among children under the age of five [...
NetworkTop-1Top-5DownloadArchitecture DenseNet 121 (k=32)74.9192.19caffemodel (30.8 MB)netscope,netron DenseNet 169 (k=32)76.0993.14caffemodel (54.6 MB)netscope,netron DenseNet 201 (k=32)77.3193.64caffemodel (77.3 MB)netscope,netron DenseNet 161 (k=48)77.6493.79caffemodel (110 MB)netscope,...
在《Rethinking the inception architecture for computer vision》中已经指出,可以在每个3×3卷积之前引入1×1卷积作为瓶颈层,以减少输入特征图的数量,从而提高计算效率。 我们发现此设计对DenseNet特别有效,我们将具有这种瓶颈的网络(即H l H_lHl的BN-ReLU-Conv(1×1)-BN-ReLU-Conv(3×3)版本)称为DenseNet...
importtensorflowastffromtensorflow.keras.applicationsimportDenseNet201# Download the DenseNet-201 model pre-trained on ImageNetmodel=DenseNet201(weights='imagenet',include_top=True)# Display a summary of the model architecturemodel.summary() 模型虽然参数量不是特别多,但是模型结果非常深。我们回顾一下 Den...