This is achieved by their use of “cardinality”, an additional dimension on top of the width and depth of ResNet. Cardinality defines the size of the set of transformations. In this image the leftmost diagram is a conventional ResNet block; the rightmost is the ResNeXt block, which has a...
DenseNet模型,它的基本思路与ResNet一致,但是它建立的是前面所有层与后面层的密集连接(dense connection),它的名称也是由此而来。DenseNet的另一大特色是通过特征在channel上的连接来实现特征重用(feature reuse)。这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能,DenseNet也因此斩获CVPR 2017的最佳...
例如,我们的250层模型仅具有15.3M参数,但是始终优于其他模型,比如FractalNet和Wide ResNets的参数超过30M。 我们还着重指出,L = 100和k = 12的DenseNet-BC与ResNet-1001性能差不多(例如,C10 +的误差为4.51%vs 4.62%,C100 +的误差为22.27%vs 22.71%),但是使用的参数量比ResNet-1001减少了90%。 图4(下图)...
We have trained and tested the HAM10000 dataset on ResNet (Khan et al. 2019), DenseNet (Sun et al. 2020), InceptionV3 (Albatayneh et al. 2020), VGG-16 (Mateen et al. 2019), and a standard CNN model, to compare the performance of the dataset on different robust CNN architectures....
We set the cross-entropy as the loss function for our models to measure the performance of the classification model. For each of the models, we used the ImageNet pre-trained weights for initialization. One has to bear in mind that the DenseNet has been trained with 1.2 million images for ...
chapter_computational-performance chapter_computer-vision chapter_convolutional-modern alexnet.md alexnet_origin.md batch-norm.md batch-norm_origin.md densenet.md densenet_origin.md googlenet.md googlenet_origin.md index.md index_origin.md nin.md nin_origin.md resnet.md resnet_o...
Adjust the Entity Name in configs/cifar10-reskanet.yaml or configs/tiny-imagenet-reskanet.yaml to Your Username or Team Name Run Update any parameters in configs and run accelerate launch cifar.py This script trains the model, validates it, and logs performance metrics using wandb on the CI...
s performance. When a coarsely annotated dataset was used, the Dense-2 U-net (Dense U-net with two blocks) achieved an average and median Dice score for the prostate of 91.2±0.8% and 90.3%, respectively,See Appendix for different versions of the Dense U-net. In addition, the Dense-2...
To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VGGNet, GoogLeNet, ResNet and ...
01-caffe_cats_vs_dogs 02-MNIST_classification_tf 03-using_densenetx files LICENSE README.md 04-Keras_GoogleNet_ResNet 05-Keras_FCN8_UNET_segmentation 07-yolov4-tutorial 08-tf2_flow 09-mnist_pyt 10-RF_modulation_recognition 11-tf2_var_autoenc ...