DeepDetect: Machine Learning Platform
DeepDetect helps software and enterprise companies deploy deep learning models via a C++11 server. It is designed for teams that manage model training and inference on-premise using Docker.
At a glance
- Category
- Software Development
- Best for
- Software Companies, Enterprise Companies, Machine Learning Engineers, Technical Operations Managers
- Pricing
- DeepDetect is open-source, and Docker builds are available for free for both CPU and GPU.
- Key use cases
- Image Analysis, Document Processing, Text and Audio Classification, Predictive Analytics
- Integrations
- Elasticsearch, AWS AMI
- Official website
- www.deepdetect.com
DeepDetect is an open-source machine learning platform designed for enterprise-level deployment. It consists of a C++11 server and a Web UI that supports the training and management of neural network models without requiring a separate database dependency.
The tool is designed for software companies and enterprise teams that integrate deep learning into their applications. It supports several machine learning libraries, including TensorFlow, Caffe, Caffe2, XGBoost, Dlib, and NCNN, and provides pre-trained models to support the training process.
Users can manage their workflow through a REST API or a Python client library. The platform supports deployment across different environments, from cloud setups to embedded devices, and supports both CPU and GPU acceleration for training and inference.
Buyers should confirm if they have the technical capacity to manage a Docker-based installation and whether the supported machine learning libraries align with their development needs.
Key Features
A C++11 based server that allows applications to communicate with machine learning models using JSON.
A browser-based interface for training and managing models, including support for Jupyter Notebooks.
Access to 25+ pre-trained models designed to support transfer learning.
Supports background training calls on multicore CPUs and GPUs to avoid blocking server communication.
Provides Docker builds for both CPU and GPU environments.
Built-in connectors for handling images, CSV, and text data.
Use Cases
Supports workflows for image tagging, object detection, and image segmentation.
Supports Optical Character Recognition (OCR) tasks.
Training and deploying models for sentiment analysis and audio or video analysis.
Supports tabular data via CSV for time-series prediction and classification.
Best For
Integrations
Pricing
DeepDetect is open-source, and Docker builds are available for free for both CPU and GPU.
FAQ
It is used to train and deploy deep learning models for tasks like image tagging, object detection, OCR, and text classification via a REST API.
The software is open-source, and Docker builds for both CPU and GPU are provided for free.
Yes, DeepDetect is designed to support operations on both multicore CPUs and GPUs.
Source category: Software Development
Source subcategory: Machine Learning Platform
