Titan rtx 24 gb

Manufactured by NVIDIA

Sourced in United States

The TITAN RTX 24 GB is a high-performance graphics processing unit (GPU) designed for professional-grade applications. It features 24 GB of GDDR6 memory and is powered by NVIDIA's Turing architecture, providing a balance of computational power and memory capacity for tasks such as AI research, data science, and 3D content creation.

Automatically generated - may contain errors

Lab products found in correlation

Spelling variants of this product

TITAN RTX (40 citations)

4 protocols using titan rtx 24 gb

YOLACT++ Object Detection Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

The annotated FFHQ data were randomly split into three sets of 3300 for training, 1100 for validation and 1100 for testing. During training, the validation set was used to evaluate the performance of the model. After training, the test set was used to evaluate the performance of the model.
YOLACT++ used in the current research was implemented on Python3, Pytorch 10.0.1 and TorchVision. TorchVision is an open library for computer vision used with PyTorch. The model based on CNN is rarely trained from scratch because it requires a relatively large dataset. Therefore, the transfer learning technique was applied to the model trained with a batch size of eight on one GUP using ImageNet pretrained weights [37 (link)]. The model was trained with the stochastic gradient descent method [38 ,39 (link),40 ] for 800,000 iterations starting at an initial learning rate of 0.001, with a momentum of 0.9 and weight decay of 0.0005, and all data augmentations used in the single-shot detector (SSD) [32 (link)] except up-side down and left/right flip were applied. The training process was conducted using a 3.0 GHz Intel Core i9-9980XE CPU, 62.5 GB RAM DDR4, GPU NVIDIA TITAN RTX 24 GB on an Ubuntu 20.04 operating system.

Lee S, & Kim J.E. (2022). Evaluating the Precision of Automatic Segmentation of Teeth, Gingiva and Facial Landmarks for 2D Digital Smile Design Using Real-Time Instance Segmentation Network. Journal of Clinical Medicine, 11(3), 852.

+ Open protocol

+ Expand

CT Image Preprocessing and Augmentation

Check if the same lab product or an alternative is used in the 5 most similar protocols

For the training data, CT images of 512 × 512 size were cropped and resized based on VOI and axial and sagittal images of 288 × 288 size were used. Augmentation was performed by randomly combining affine transformation, crop, and cutout³³. Xavier uniform initialization⁴⁰ and an Adam optimizer were used for network weight initialization and optimization, respectively, with the learning rate set at 3e−4. We set the scheduler's patience to 30 and decreased the learning rate by multiplying by 0.1 every 10 epochs. The network was trained for 100 epochs using Intel^® Core™ i7-8700 3.20 GHz processor, 32 GB RAM memory, and TITAN RTX 24 GB (NVIDIA, Santa Clara, CA, USA).

Park T., Yoon M.A., Cho Y.C., Ham S.J., Ko Y., Kim S., Jeong H, & Lee J. (2022). Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Scientific Reports, 12, 6735.

+ Open protocol

+ Expand

Deep Neural Network Classifier Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

To construct our DNN model, we utilized TensorFlow 1.13.1 as our machine learning library with Python 3.6.0 [16 ].
We chose tf.contrib.learn.DNNClassifier for model construction. For the hyperparameters of our model, we set the dropout rate at 0.15, we chose the Adam optimizer, and we fixed the learning rate at 1e-5. The activation function was leaky_relu and the number of layers was 4. The numbers of neurons of the layers were 512, 256, 128, and 16, respectively (Fig. 2). When we constructed our model under these hyperparameter settings, we set the number of learning steps as 5,000. An Nvidia Titan RTX 24GB was used for the GPU.
In order to obtain measurements for the performance of our model, accuracy was calculated using the predicted values from the training set and the test set; then, receiver operating characteristic curves and the area under the curve (AUC) were obtained by the roc-curve function in the scikit-learn package.

Park A, & Nam S. (2019). Deep learning for stage prediction in neuroblastoma using gene expression data. Genomics & Informatics, 17(3), e30.

+ Open protocol

+ Expand

Explainable AI-Powered Sepsis Prediction

Cited 1 time

Check if the same lab product or an alternative is used in the 5 most similar protocols

We trained models on vectorized drug relationships and selected lab test types, along with the common EHRs that are widely used in the reported sepsis prediction models [16 (link),17 (link)]. We considered 2 machine learning models comprising logistic regression (LR) [21 (link)] and random forest [22 (link)] and 3 deep learning models comprising artificial neural networks (ANNs) [23 (link)], residual convolutional neural networks (ResNet10) [24 (link)], and long short-term memory recurrent neural networks (RNN-LSTMs) [25 ]. When applied to the model, the data were reshaped to (1, 42, 42) for the ResNet10 model and padded to the maximum length of the sequence and reshaped to (number of patients, time sequence, number of features) for the LSTM model. We investigated the important features using Shapley Additive Explanations (SHAP) [26 ]. SHAP, one of the Explainable Artificial Intelligence (XAI) techniques, is a method used to interpret results from deep learning and machine learning models and is based on game theory. We used Tree SHAP explainer to calculate the Shapley values.
All proposed approaches were implemented using the Python 3.7 library, such as PyTorch 1.5, Scikit-learn, and SHAP, on an NVIDIA TITAN RTX 24 GB × 2. The source code is available on GitHub [27 ].

Yang D., Kim J., Yoo J., Cha W.C, & Paik H. (2022). Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning–Based Approach. JMIR Medical Informatics, 10(6), e37689.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!