The previously release Docker images for MMDetection only worked with PyTorch 1.3 on CUDA 10.1, which only worked on NVIDIA 2080 Ti cards, but not 1080 Ti ones. Today, we released a new Docker image which is based on PyTorch 1.2 and CUDA 10.0, which allows 1080 Ti cards to be used again:
New Docker images are available for the MMDetection object detection framework, using the 1.2.0 (code base as of 2020-03-01) release of MMDetection:
The first Docker images are available for the YOLACT (You Only Look At CoefficienTs) image segmentation model, using the 1.2 release:
YOLACT/YOLACT++, like MMDetection, is based on PyTorch. In contrast to MMDetection it is not a divers framework, but just a single model, which can make use of various base models (e.g., ResNet50). Similar to Mask R-CNN, it generates a mask with probabilities for each identified object, which requires post-processing to determine a polygon or minimal rectangle. However, it is much faster than the Mask R-CNN implementation that TensorFlow's Object Detection API offers (roughly 3x faster on 1000x300 images).
The first Docker images are available for the MMDetection object detection framework, using the 2019-11-30 code base (close to 1.0rc1) of MMDetection:
MMDetection is based on PyTorch rather than TensorFlow and offers a larger number of pre-trained models and example configurations than TensorFlow's Object Detection API. It is developed by the Multimedia Laboratory of the Chinese University of Hong Kong.
The wai.tfutils library simply centralizes a bunch of TensorFlow related operations to code duplication:
Our project on user-friendly deep-learning received funding for three years.
Read more on Scoop: MBIE-funded University research could open up new technology