Search Results for "model-builder"

...It is designed to showcase the emerging field of RF-based sensing, where machine learning models interpret wireless channel data to reconstruct human movement and posture. The repository includes components for data processing, model inference, and real-time visualization, making it suitable for research and experimental deployments. Its architecture emphasizes performance and reproducibility, allowing developers to explore non-visual motion capture systems using accessible hardware. Overall, WiFi DensePose functions as an advanced research-grade toolkit for WiFi-based human sensing and pose estimation.

...In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour).

Sentry is a cross-platform, self-hosted error monitoring solution that helps software teams discover, monitor and fix errors in real-time. The most users and logs will have to provide are the clues, and Sentry provides the answers. Sentry offers enhanced application performance monitoring through information-laden stack traces. It lets you build better software faster and more efficiently by showing you all issues in one place and providing the trail of events that lead to errors. It also...

...It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior network is needed after all. And so research continues. For simpler training, you can directly supply text strings instead of precomputing text encodings. ...

Image of OpenWrt OS, with snort community featured in, for Raspberry Pi 4/ 4B+ and 400, basically for Processor BCM2711. Installed Snort Community Model to Intrusion Detection system. Prevention system not installed. eth0 used as output/ LAN socket, to run internet and access router. eth1, which can be USB -> Ethernet port, is used as WAN port, to connect Pi board to Internet IP. to flash img file, you can restore in SD card, or use Balena Etcher to flash. or you can use DD commend of UNIX. ...

INFORMATION This program can be used for the following: - To collecting Cisco device information like: hostname, serial, model, IOS image, version, uptime, free memory (to CSV) - To enable the SCP service on the device (automaticly before file transfers) - To make a backup of the running-config and startup-config with SCP (with built-in MD5 hash comparison check) no TFTP/SFTP server needed. - To save the running-config to the startup-config (write memory) - To transfer a new image to the device with SCP (with built-in MD5 hash comparison check) - To change the boot target to the new image (boot system flash:/) It has got the following features: - Error handling - information and error logging to log file (Log will be appended) - information and error logging to CSV file (Log will be overwritten each run) - Transfers retry 3 times - MD5 checks after file transfers to be sure the file is transferred without problems

...Performs better than LSTM/GRU on a vast range of tasks (Seq. MNIST, Adding Problem, Copy Memory, Word-level PTB...). Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the maximum number of steps back in time from current sample at time T, that a filter from (block, layer, stack, TCN) can hit (effective history) + 1. ...

...Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. Please set the margin argument to 0 for tight cropping. You can evaluate a trained model on the APPA-REAL (validation) dataset. We pose the age regression problem as a deep classification problem followed by a softmax expected value refinement and show improvements over direct regression training of CNNs. Our proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face.

...This project aims to make the expanse of human knowledge beautifully presentable and the exabytes of data navigable by an average user via the power of a Unified Data Model and 3d visualization layer for the Web. It will invert the top 3 layers of the OSI network model to make a 3-dimensional presentation layer with a peer-to-peer session layer for the Internet. For the curious, there is a simple demo that provides a small sense of the possibilities. Requires: VPython available here or see http://vpython.org.

Open TBroker aims to provide an open source implementation of a Tunnel Broker according to the model specified by RFC3053.

A metering and charge-back mechanism based on real time pricing (RTP) for the cloud services. This project caters to the need of a dynamic pay-per-use model in the cloud environment.