Difference between revisions of "Computer Vision"
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to list the available versions on HiPerGator-AI. Please see recorded [https://help.rc.ufl.edu/doc/Monai MONAI Core tutorials] for details. | to list the available versions on HiPerGator-AI. Please see recorded [https://help.rc.ufl.edu/doc/Monai MONAI Core tutorials] for details. | ||
− | *'''MONAI label''': MONAI Label is an intelligent open source image labeling and learning tool that enables users to create annotated datasets and build AI annotation models. MONAI Label reduces the time and effort of annotating new datasets and enables the adaptation of AI to the task at hand by continuously learning from user interactions and data. | + | *'''MONAI label''': MONAI Label is an intelligent open source image labeling and learning tool that enables users to create annotated datasets and build AI annotation models. MONAI Label reduces the time and effort of annotating new datasets and enables the adaptation of AI to the task at hand by continuously learning from user interactions and data. |
+ | Use | ||
+ | ngc-monailabel | ||
+ | to list the available versions on HiPerGator-AI. | ||
+ | Please see [https://help.rc.ufl.edu/doc/Monai MONAI Label tutorials] for details. |
Revision as of 15:15, 30 August 2022
Description
This page describes the collection of Computer Vision software on HiperGator. Computer vision is a crucial field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs. Research Computing AI support will help with building new and finetuning existing advanced models for various tasks including image classification, object detection, semantic segmentation, etc. via support requests or consulting.
Deep Learning Frameworks
There are preinstalled and configured environments on HiPerGator for the most frequently used deep learning frameworks including tensorflow, pytorch, and MxNet. The available deep learning frameworks can be queried using
module spider [pytorch/tensorflow/mxnet]
Users can also customize their own conda environment following this tutorial https://github.com/magitz/conda_Jupyter_kernels.
Module environments for Computer Vision
Below are a number of module environments or framework that is configured for training or your deep learning models on HiPerGator-AI.
- tensorflow or ngc-tensorflow: TensorFlow is a free and open-source software library for machine learning and artificial intelligence. ngc-tensorFlow is the NVIDIA optimized environment container for training on NVIDIA GPUs. Use
module spider tensorflow
to list the available versions on HiPerGator-AI.
- pytorch or ngc-pytorch: PyTorch is an open source machine learning framework based on the Torch library. ngc-pytorch is the NVIDIA optimized environment container for training on NVIDIA GPUs. Use
module spider pytorch
to list the available versions on HiPerGator-AI.
- Matlab: Matlab provides rich libraries of image processing techniques for image analysis.
- Detectron2: Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms.
Modules for Medical Imaging Applications
- MONAI core: MONAI Core is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Use
module spider monai
to list the available versions on HiPerGator-AI. Please see recorded MONAI Core tutorials for details.
- MONAI label: MONAI Label is an intelligent open source image labeling and learning tool that enables users to create annotated datasets and build AI annotation models. MONAI Label reduces the time and effort of annotating new datasets and enables the adaptation of AI to the task at hand by continuously learning from user interactions and data.
Use
ngc-monailabel
to list the available versions on HiPerGator-AI. Please see MONAI Label tutorials for details.