Biocat is a java based software that allows to perform image classification or segmentation using machine learning. Several algorithm for the classification are available.
ZEN and APEER – Open Ecosystem for integrated Machine-Learning Workflows
Open ecosystem for integrated machine-learning workflows to train and use machine-learning models for image processing and image analysis inside the ZEN software or on the APEER cloud-based platform
Highlights ZEN
Simple User Interface for Labeling and Training
Engineered Features Sets and Deep Feature Extraction + Random Forrest for Semantic Segmentation
Object Classification workflows
Probability Thresholds and Conditional Random Fields
Import your own trained models as *.czann files (see: czmodel · PyPI)
Import "AIModel Containes" from arivis AI for advanced Instance Segmentation
Integration into ZEN Measurement Framework
Support for Multi-dimensional Datasets and Tile Images
open and standardized format to store trained models
ZEN Intellesis Segmentation
ZEN Intellesis - Pretrained Networks
Intellesis Object Classification
Highlights Aarivis AI
Web-based tool to label datasets to train Deep Neural Networks
Fully automated hyper-parameter tuning
Export of trained models for semantic segmentation and AIModelContainer for Instance Segmentation
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter.
