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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
This innovative tool is designed for quantizing convolutional neural networks (CNNs). It allows for the transformation of both weights/biases and activations from 32-bit floating-point (FP32) to 8-bit integer (INT8) format, or even other bit depths. Utilizing this tool can greatly enhance inference performance and efficiency, all while preserving accuracy levels. It is compatible with various common layer types found in neural networks, such as convolution, pooling, fully-connected layers, and batch normalization, among others. Remarkably, the quantization process does not require the network to be retrained or the use of labeled datasets; only a single batch of images is sufficient. Depending on the neural network's size, the quantization can be completed in a matter of seconds to several minutes, facilitating quick updates to the model. Furthermore, this tool is specifically optimized for collaboration with DeePhi DPU and can generate the INT8 format model files necessary for DNNC integration. By streamlining the quantization process, developers can ensure their models remain efficient and robust in various applications.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$0.90 per hour
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
DeePhi Quantization Tool
Website
aws.amazon.com/marketplace/pp/prodview-bwtx6kzwg3gva