DL models were developed on a workstation with a single Intel Xeon E5-2650 v4 CPU @ 2.2 GHz, 128 GB RAM, 4 NVIDIA TITAN V GPUs. The operating system was Ubuntu 18.04 LTS. All code used in this study was written in Python v3.6, and DL models were implemented by using PyTorch v1.3. Image preprocessing was performed using the Python Imaging Library (Python Imaging Library). We used ImageNet pretrained weights to initialize the backbone network DenseNet-16946 . The Adam optimizer47 was used to train the model for 100 epochs with a batch size of 8 and a starting learning rate of 1e−5. The trained model was evaluated on the validation set after every training epoch, and the one with the highest validation AUROC was selected as the best model.
Xeon e5 2650 v4
Manufactured by NVIDIA
The Xeon E5-2650 v4 is a server-grade processor developed by Intel. It features 12 cores, 24 threads, and a base clock speed of 2.2 GHz. The processor is designed for use in high-performance computing applications and data centers.
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Lab products found in correlation
2 protocols using xeon e5 2650 v4
1
DenseNet-169 Image Classification Model
2
Deep Learning Model Optimization Protocol
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The proposed models were implemented by Pytorch framework on Linux operating
system using Python Application Programmable Interface and then accelerated
by the NVIDIA graphics card. To prevent overfitting, a batch normalization
operation was performed after each convolution operation. And the Kaiming
function was used to configure network initialization parameters. In the
training stage, the learning rate was set to 0.00015 in the Adam optimizer,
the batch size was set to 2, and the mixed loss function was the training
loss function. The max number of epochs was 90, and the loss value decreased
with the epoch number. After each training epoch, validation was performed
on the validation set, and only the best parameters would be saved. All
experiments in this article were performed on Intel Xeon E5-2650 V4 (2.2GHz)
processor and NVIDIA tesla T4 graphics card.
system using Python Application Programmable Interface and then accelerated
by the NVIDIA graphics card. To prevent overfitting, a batch normalization
operation was performed after each convolution operation. And the Kaiming
function was used to configure network initialization parameters. In the
training stage, the learning rate was set to 0.00015 in the Adam optimizer,
the batch size was set to 2, and the mixed loss function was the training
loss function. The max number of epochs was 90, and the loss value decreased
with the epoch number. After each training epoch, validation was performed
on the validation set, and only the best parameters would be saved. All
experiments in this article were performed on Intel Xeon E5-2650 V4 (2.2GHz)
processor and NVIDIA tesla T4 graphics card.
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