Titan rtx

Manufactured by NVIDIA

Sourced in United States

The TITAN RTX is a high-performance graphics processing unit (GPU) designed for professionals and researchers. It features 24GB of GDDR6 memory and is powered by NVIDIA's Turing architecture, providing significant computational capabilities for tasks such as scientific computing, data analysis, and AI research.

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Titan RTX GPU (57 citations) TITAN RTX 24 GB (4 citations) Titan RTX graphics card (4 citations) TITAN RTX 24 G (3 citations) GeForce RTX 2080 Titan GPU memory (2 citations) TITAN RTX GPU card (2 citations) Geforce TITAN RTX GPU (2 citations) TITAN RTX 24GB GPU (2 citations)

40 protocols using titan rtx

Weakly Supervised Tooth-Marked Tongue Detection

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The proposed weakly supervised tooth-marked tongue detection model was implemented using “PyTorch” (pytorch.org) and the Adam algorithm was used to minimize the objective function. Data augmentation was implemented with torchvision and image augmentation (github.com/aleju/imgaug). We used a NVIDIA TITAN RTX graphics card with 24G memory. The initialization of the learning rate was set to 1e-4 and the weight decay was set to 1e-4, batchsize was set to 32. The performance metrics of the computer are as follows: CPU is Intel(R) Xeon(R) Gold 5118. RAM is 64.0 GB. GPU is NVIDIA TITAN RTX. Since the computation time per image is too short and inconsistent, we selected 20 tongue images, in which there are 10 tooth-marked images and 10 non-tooth-marked images, and the total time for generating the tooth mark candidate area is calculated to obtain the time of each picture. To alleviate the problem of overfitting, if the validation accuracy did not increase for 10 epoch, an early stop was used to stop the optimization and save the model weight. Basic implementation code of the work can be available in GitHub, https://github.com/Lsx0802/WSTMD.

Zhou J., Li S., Wang X., Yang Z., Hou X., Lai W., Zhao S., Deng Q, & Zhou W. (2022). Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition. Frontiers in Physiology, 13, 847267.

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DeepEthogram: Deep Learning for Animal Behavior Analysis

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We implemented DeepEthogram in the Python programming language (version 3.7 or later; Rossum et al., 2010 ). We used PyTorch (Paszke, 2018 ; version 1.4.0 or greater) for all deep-learning models. We used PyTorch Lightning for training (Falcon, 2019 ). We used OpenCV (Bradski, 2008 ) for image reading and writing. We use Kornia (Riba et al., 2019 ) for GPU-based image augmentations. We used scikit-learn (Pedregosa, 2021 ) for evaluation metrics, along with custom Python code. CNN diagram in Figure 1 was generated using PlotNeuralNet (Iqbal, 2018 (link)). Other figures were generated in Matplotlib (Caswell, 2021 (link)). For training, we used one of the following Nvidia GPUs: GeForce 1080Ti, Titan RTX, Quadro RTX6000, or Quadro RTX8000. Inference speed was evaluated on a computer running Ubuntu 18.04, an AMD Ryzen Threadripper 2950 X CPU, an Nvidia Titan RTX, an Nvidia GeForce 1080Ti, a Samsung 970 Evo hard disk, and 128 GB DDR4 memory.

Bohnslav J.P., Wimalasena N.K., Clausing K.J., Dai Y.Y., Yarmolinsky D.A., Cruz T., Kashlan A.D., Chiappe M.E., Orefice L.L., Woolf C.J, & Harvey C.D. (2021). DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels. eLife, 10, e63377.

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GPU-Powered Machine Learning Protocols

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Models were trained on NVIDIA TITAN Xp (12 GB), except for scVI on the mouse brain (1M) dataset, which was trained on NVIDIA TITAN RTX (24 GB).

Schuster V, & Krogh A. (2023). The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data. Bioinformatics, 39(9), btad497.

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Wrinkle Pattern Extraction from Microscope Images

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We use a method SW-UNet¹⁹, which is a CNN based on U-Net⁴⁴ to extract wrinkle patterns from the microscope image as shown in Fig. 1(b). As the training data, we prepare 236 sets of corresponding two images (microscope image and manually labeled wrinkle image). The number of data is increased to 2596 by using the image augmentation techniques. We used NVIDIA Titan RTX to accelerate the training process, and the Adam optimizer is utilized. Note that the procedure until Step 2 was already developed and utilized in our previous papers^{19 ,20 (link)}, and GAN-based force estimation is newly presented in this work.

Li H., Matsunaga D., Matsui T.S., Aosaki H., Kinoshita G., Inoue K., Doostmohammadi A, & Deguchi S. (2022). Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images. Communications Biology, 5, 361.

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LSTM Model for Shuttlecock Position Prediction

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We implemented the proposed method in PyTorch [39 ] (1.12.1+cu102, with Python 3.7.13) and ran it on the NVIDIA TITAN RTX processing unit using CUDA 11.4. For LSTM, the number of layers was set to 3, the hidden layer to 128 dimensions, and the network was optimized using Adam [40 ], with a weight decay of

10^{- 4}

, the momentums

β_{1} = 0.5

and

β_{2} = 0.999

, and a learning rate of 0.02. The model was trained for 400 epochs with four input frames and 12 output frames for all cases.
The mean squared error (MSE) was employed as the loss function:

L o s s = \frac{1}{D} \sum_{i = 1}^{D} {({F (x)}_{i} - {g t}_{i})}^{2},

where F is the time-series model LSTM of the proposed method, x is its input data and

g t

is the ground-truth data of the output shuttlecock position.

F (x)

and

g t

are D-dimensional vectors and

{F (x)}_{i}

is the value of

F (x)

on the i dimension. The output results were compared with the ground-truth data to calculate the error. Then, the parameters were updated to reduce the error using backpropagation.

Nokihara Y., Hachiuma R., Hori R, & Saito H. (2023). Future Prediction of Shuttlecock Trajectory in Badminton Using Player’s Information. Journal of Imaging, 9(5), 99.

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Segmentation Model with Adam Optimizer

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The Adam optimizer was used with L2 penalty 0.00001, β₁ = 0.9, β₂ = 0.999 and initial learning rate 0.01. The learning rate was decayed by a factor of 0.5 every 50 epochs. Inspired by Milletari et al.,¹⁸ we used one minus mean of Dice coefficients from all labels as loss function during training, with equal weight (w_FG = 1) for all foreground labels and decayed weight (w_BG = 0.1) for the background. With a batch size of 2, we trained our model for 1000 total epochs and early stop was used in the training process. With the early stop, the training process is finished around 400 epochs and each epoch took 97 seconds. Total number of parameters of model is 36,290,202. The whole training process was conducted on an NVIDIA Titan RTX with 24 GB memory and was implemented using PyTorch.

Li H., Zhang H., Johnson H., Long J.D., Paulsen J.S, & Oguz I. (2021). MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs. Proceedings of SPIE--the International Society for Optical Engineering, 11596, 115960W.

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Machine Learning Framework with Python

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The analysis was performed on a terminal using Ubuntu 20·04·2 LTS, with an Intel Core i9-9960X CPU, 64 GB of primary memory, and two NVIDIA TITAN RTX GPUs with a total RAM of 48 GB that were linked using an NVLink bridge. The machine learning framework consisted of Python (3·6·9), Gensim (3·8·3), and scikit-learn (0·24 2), which is a module for machine learning in Python. We used FastText (0·9·2) and GloVe (1·2).

Yokokawa D., Noda K., Yanagita Y., Uehara T., Ohira Y., Shikino K., Tsukamoto T, & Ikusaka M. (2022). Validating the representation of distance between infarct diseases using word embedding. BMC Medical Informatics and Decision Making, 22, 322.

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Deep Learning for Velocity Tracing

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For the quality task, we used the publicly available ResNet 34¹⁷ with a single output neuron. Training data were augmented using random scaling, translation, and Gamma correction. For the velocity-tracing task, we used HighHRNet-W32¹⁸ optimized for pixel level segmentation. The expert tracing of the envelope of Doppler spectrum was converted into a heat map with a Gaussian distribution with a standard deviation of two pixels. Both networks were trained using batches of 16 images split across 4 NVIDIA Titan RTX graphical processing units (NVIDIA Corporation, DE, USA) using the Adam optimizer^{19 (link)} and the mean-squared error loss function. They were trained for 200 and 400 epochs, respectively. Initial learning rates were set and then modulated using the ‘OneCycle’ learning rate scheduler.²⁰

Seligman H., Patel S.B., Alloula A., Howard J.P., Cook C.M., Ahmad Y., de Waard G.A., Pinto M.E., van de Hoef T.P., Rahman H., Kelshiker M.A., Rajkumar C.A., Foley M., Nowbar A.N., Mehta S., Toulemonde M., Tang M.X., Al-Lamee R., Sen S., Cole G., Nijjer S., Escaned J., Van Royen N., Francis D.P., Shun-Shin M.J, & Petraco R. (2023). Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment. European Heart Journal. Digital Health, 4(4), 291-301.

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Segmentation Task with TensorFlow 1.13.1

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The realization of the segmentation task is based on tensorflow 1.13.1. In addition, we used the Adam optimizer to train the model. The entire network was trained with a total of 500,000 steps and the training set was traversed 10 times. After each traversal of the training set, the order of the data will be randomly shuffled to enhance the robustness of training. The initial learning rate is set to 0.0001, which is reduced to half of the original learning rate after traversing the training set twice. Finally, Mean Dice is used as the evaluation index of training, and the loss value and accuracy index are output every ten steps to realize effective supervision of model training. At the same time, the parameter model is saved every 1000 steps.
The experimental environment is run on TensorFlow. The runtime platform processor is Intel (R) Xeon (R) Silver 4210 CPU @2.20GHz 2.20GHz 128GB RAM, Nvidia Titan RTX, 64-bit Windows10.The development software platform is PyCharm with Python 3.6.

Huang H., Zhang W., Fang Y., Hong J., Su S, & Lai X. (2021). Overall Survival Prediction for Gliomas Using a Novel Compound Approach. Frontiers in Oncology, 11, 724191.

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Sensor Velocity Estimation from OBD Data

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The model was trained on a computer running the Windows 10 operating system. It was equipped with an Intel Xeon W-2445 CPU processor with 3.7 GHz, 64 GB RAM, and a NVIDIA TITAN RTX GPU graphics card with 24 GB. The average run time for a single inference was

0.035 s

during the evaluation. This time considers training with 422,680 (70%) samples, 90,574 samples (15%) for validation, and 90,574 samples (15%) for the test. The input data for each sample consider the OBD velocity as a feature. The input length is 5 for the baseline training and is varied for the input length ablation study. The learning target of each sample considers the reference sensor velocity as feature and has a length of 2.

Flores Fernández A., Sánchez Morales E., Botsch M., Facchi C, & García Higuera A. (2022). Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics. Sensors (Basel, Switzerland), 23(1), 159.

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