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Description
Darknet is a neural network framework that is open-source, developed using C and CUDA. Known for its speed and simplicity in installation, it accommodates both CPU and GPU processing. The source code is available on GitHub, where you can also explore its capabilities further. The installation process is straightforward, requiring only two optional dependencies: OpenCV for enhanced image format support and CUDA for GPU acceleration. While Darknet performs efficiently on CPUs, it boasts a performance increase of approximately 500 times when running on a GPU! To leverage this speed, you'll need an Nvidia GPU alongside the CUDA installation. By default, Darknet utilizes stb_image.h for loading images, but for those seeking compatibility with more obscure formats like CMYK jpegs, OpenCV can be employed. Additionally, OpenCV provides the functionality to visualize images and detections in real-time without needing to save them. Darknet supports the classification of images using well-known models such as ResNet and ResNeXt, and it has become quite popular for employing recurrent neural networks in applications related to time-series data and natural language processing. Whether you're a seasoned developer or a newcomer, Darknet offers an accessible way to implement advanced neural network solutions.
Description
Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.
API Access
Has API
API Access
Has API
Integrations
3LC
AWS Marketplace
Amazon EC2 P4 Instances
Amazon Elastic Inference
Amazon SageMaker Unified Studio
DagsHub
Groq
Label Studio
Microsoft Azure
NVIDIA TensorRT
Integrations
3LC
AWS Marketplace
Amazon EC2 P4 Instances
Amazon Elastic Inference
Amazon SageMaker Unified Studio
DagsHub
Groq
Label Studio
Microsoft Azure
NVIDIA TensorRT
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Darknet
Website
pjreddie.com/darknet/
Vendor Details
Company Name
PyTorch
Founded
2016
Website
pytorch.org
Product Features
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization