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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

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Description

Caffe is a deep learning framework designed with a focus on expressiveness, efficiency, and modularity, developed by Berkeley AI Research (BAIR) alongside numerous community contributors. The project was initiated by Yangqing Jia during his doctoral studies at UC Berkeley and is available under the BSD 2-Clause license. For those interested, there is an engaging web image classification demo available for viewing! The framework’s expressive architecture promotes innovation and application development. Users can define models and optimizations through configuration files without the need for hard-coded elements. By simply toggling a flag, users can seamlessly switch between CPU and GPU, allowing for training on powerful GPU machines followed by deployment on standard clusters or mobile devices. The extensible nature of Caffe's codebase supports ongoing development and enhancement. In its inaugural year, Caffe was forked by more than 1,000 developers, who contributed numerous significant changes back to the project. Thanks to these community contributions, the framework remains at the forefront of state-of-the-art code and models. Caffe's speed makes it an ideal choice for both research experiments and industrial applications, with the capability to process upwards of 60 million images daily using a single NVIDIA K40 GPU, demonstrating its robustness and efficacy in handling large-scale tasks. This performance ensures that users can rely on Caffe for both experimentation and deployment in various scenarios.

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.

Description

TFlearn is a flexible and clear deep learning framework that operates on top of TensorFlow. Its primary aim is to offer a more user-friendly API for TensorFlow, which accelerates the experimentation process while ensuring complete compatibility and clarity with the underlying framework. The library provides an accessible high-level interface for developing deep neural networks, complete with tutorials and examples for guidance. It facilitates rapid prototyping through its modular design, which includes built-in neural network layers, regularizers, optimizers, and metrics. Users benefit from full transparency regarding TensorFlow, as all functions are tensor-based and can be utilized independently of TFLearn. Additionally, it features robust helper functions to assist in training any TensorFlow graph, accommodating multiple inputs, outputs, and optimization strategies. The graph visualization is user-friendly and aesthetically pleasing, offering insights into weights, gradients, activations, and more. Moreover, the high-level API supports a wide range of contemporary deep learning architectures, encompassing Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, making it a versatile tool for researchers and developers alike.

API Access

Has API Yes 

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Screenshots View All

Integrations

AWS Elastic Fabric Adapter (EFA) Yes 
AWS Marketplace Yes 
Amazon Web Services (AWS) Yes 
Docker Yes 
Fabric for Deep Learning (FfDL) Yes 
Lambda Yes 
NVIDIA DIGITS Yes 
OpenVINO Yes 
Polyaxon Yes 
Pop!_OS Yes 
TensorFlow No 
Zebra by Mipsology Yes 

Integrations

AWS Elastic Fabric Adapter (EFA) No 
AWS Marketplace No 
Amazon Web Services (AWS) No 
Docker No 
Fabric for Deep Learning (FfDL) No 
Lambda No 
NVIDIA DIGITS No 
OpenVINO No 
Polyaxon No 
Pop!_OS No 
TensorFlow No 
Zebra by Mipsology No 

Integrations

AWS Elastic Fabric Adapter (EFA) No 
AWS Marketplace No 
Amazon Web Services (AWS) No 
Docker No 
Fabric for Deep Learning (FfDL) No 
Lambda No 
NVIDIA DIGITS No 
OpenVINO No 
Polyaxon No 
Pop!_OS No 
TensorFlow Yes 
Zebra by Mipsology No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

$0.90 per hour
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

BAIR

Country

United States

Website

caffe.berkeleyvision.org

Vendor Details

Company Name

DeePhi Quantization Tool

Website

aws.amazon.com/marketplace/pp/prodview-bwtx6kzwg3gva

Vendor Details

Company Name

TFLearn

Website

tflearn.org

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification Yes 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization Yes 

Product Features

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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