1.23ms Inference Time 0 ‑ 25MB Memory Usage 312NPU Layers See more metrics Model RepositoryHugging FaceResearch Paper Technical Details Model checkpoint:Imagenet Input resolution:224x224 Number of parameters:7.97M Model size:30.5 MB Applicable Scenarios ...
Table 5shows the number of parameters, training and test times, and size of each model. DenseNet121 had the highest accuracy, but also the longest test time, 5.05 ms. Although VGG19 had fewer parameters to learn than DenseNet121, its training time was the longest due to the large number ...
The dynamic convolutional kernel in the figure refers to the following process: the size of the convolutional kernel is adaptively changed by a function; in the layer with a large number of channels, use the larger convolutional kernel to carry out 1 × 1 convolution to make more cross-channel...
On the large scale ILSVRC 2012 (ImageNet) dataset, DenseNet achieves a similar accuracy as ResNet, but using less than half the amount of parameters and roughly half the number of FLOPs.Figure 1: A dense block with 5 layers and growth rate 4....
I have one more question related to deep. In your implementation, you useddeep=L=3n+4. I understood that3means the number dense block,nis the number of layers in each dense block. These layers are (BN+Scale+ReLU), (Conv) and (Dropout). However, what is4? I guess is is 1conv+ 2...
Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements ...
We also use grouped convolution instead of standard convolution in dense layers to lower the number of model parameters and improve efficiency. Additionally, we substitute the ReLU (Rectified Linear Unit) activation function with the ELU (Exponential Linear Unit) activation function, which reduces the...
number of convolution kernels remains N, with N/G kernels per group, convolving only with the respective group’s input feature maps. The total number of parameters for the convolution kernels is N × (C/G) × K × K, significantly reducing the overall parameter count to 1/G of the ...
This convolution operation can greatly reduce the number of parameters while ensuring accuracy. Deep separable convolution is also used in Xception network structure, so its performance is better than EfficientNet. Its confusion matrix is shown in Figure 6. It can be seen that category 1 (plant) ...