Support new platforms, including Kria KV260 SoM kit and Versal ACAP platforms VCK190, VCK5000;
Extended Pytorch framework support from version 1.5 to version 1.7.1;
Added new state-of-the-art models, including 4D Radar detection, Image-Lidar sensor fusion, 3D detection & segmentation, multi-task, depth estimation, super resolution and more models that applicable to automotive, smart medical, industrial vision applications;
Easier subgraph partition user experience with the new Graph Runner API;
Vitis AI 1.4 What’s New by Category
Expand the sections below to learn more about the new features and enhancements in Vitis AI 1.4.
Added 16 new models, and total 108 models from different deep learning frameworks (Caffe, TensorFlow, TensorFlow 2 and PyTorch) are provided.
Increased the diversity of models compared to Vitis AI 1.3:
For autonomous driving and ADAS, added 4D Radar detection, Image-Lidar sensor fusion, surround-view 3D detection, upgraded 3D segmentation and multi-task models
For medical and industrial vision, added depth estimation, RGB-D segmentation, super-resolution and other reference models
EoU enhancement: provided automated download scripts for free selection of the versions according to model name and hardware platform
Support fast finetune in post-training quantization (PTQ);
Improved quantize-aware training(QAT) functions:
Support more layers: swish/sigmoid, hard-swish, hard-sigmoid, LeakyRelu, nested tf.keras functional and sequential models