Vitis AI Library 1.2 Release Notes - 1.3 English

Vitis AI Library User Guide (UG1354)

Document ID
UG1354
Release Date
2021-02-03
Version
1.3 English

This section contains information regarding the features and updates of the Vitis™ AI Library 1.2 release.

Key Features And Enhancements

This AI Library release includes the following key features and enhancements:

New Cloud Board Support
Alveo™ U50LV and U280 cards are now supported in this release.
New Model Libraries
The following new model libraries are supported.
  • face recognition
  • plate detection
  • plate recognition
  • medical segmentation
PyTorch Model Support
Three PyTorch models are supported.
Support for new Caffe Models
Six new Caffe models are supported.

Changes

  • The installation mode of the target for the Edge is changed and the RPM format package is used.
  • meta.json file in the model has been deprecated.

Compatibility

The Vitis™ AI Library 1.2 is tested with the following images.

  • xilinx-zcu102-dpu-v2020.1-v1.2.0.img.gz
  • xilinx-zcu104-dpu-v2020.1-v1.2.0.img.gz

Model Support

The following models are supported by this version of the Vitis™ AI Library.

Table 1. Model Supported by the AI Library
No. Neural Network ZCU102/ZCU104 U50/U50LV/U280 Application
1 inception_resnet_v2_tf Y Y Image Classification
2 inception_v1_tf Y Y
3 inception_v3_tf Y Y
4 inception_v4_2016_09_09_tf Y Y
5 mobilenet_v1_0_25_128_tf Y N/A
6 mobilenet_v1_0_5_160_tf Y N/A
7 mobilenet_v1_1_0_224_tf Y N/A
8 mobilenet_v2_1_0_224_tf Y N/A
9 mobilenet_v2_1_4_224_tf Y N/A
10 resnet_v1_101_tf Y Y
11 resnet_v1_152_tf Y Y
12 resnet_v1_50_tf Y Y
13 vgg_16_tf Y Y
14 vgg_19_tf Y Y
15 ssd_mobilenet_v1_coco_tf Y N/A Object Detection
16 ssd_mobilenet_v2_coco_tf Y N/A
17 ssd_resnet_50_fpn_coco_tf Y Y
18 yolov3_voc_tf Y Y
19 mlperf_ssd_resnet34_tf Y N/A
20 resnet50 Y Y Image Classification
21 resnet18 Y Y
22 inception_v1 Y Y
23 inception_v2 Y Y
24 inception_v3 Y Y
25 inception_v4 Y Y
26 mobilenet_v2 Y N/A
27 squeezenet Y Y
28 ssd_pedestrian_pruned_0_97 Y Y ADAS Pedestrian Detection
29 ssd_traffic_pruned_0_9 Y Y Traffic Detection
30 ssd_adas_pruned_0_95 Y Y ADAS Vehicle Detection
31 ssd_mobilenet_v2 Y N/A Object Detection
32 refinedet_pruned_0_8 Y Y
33 refinedet_pruned_0_92 Y Y
34 refinedet_pruned_0_96 Y Y
35 vpgnet_pruned_0_99 Y Y ADAS Lane Detection
36 fpn Y Y ADAS Segmentation
37 sp_net Y Y Pose Estimation
38 openpose_pruned_0_3 Y Y  
39 densebox_320_320 Y Y Face Detection
40 densebox_640_360 Y Y
41 face_landmark Y Y Face Detection and Recognition
42 reid Y Y Object tracking
43 multi_task Y Y ADAS
44 yolov3_adas_pruned_0_9 Y Y Object Detection
45 yolov3_voc Y Y
46 yolov3_bdd Y Y
47 yolov2_voc Y Y
48 yolov2_voc_pruned_0_66 Y Y
49 yolov2_voc_pruned_0_71 Y Y
50 yolov2_voc_pruned_0_77 Y Y
51 facerec_resnet20 Y Y Face Recognition
52 facerec_resnet64 Y Y
53 plate_detection Y Y Plate Recognition
54 plate_recognition Y Y
55 FPN_Res18_Medical_segmentation Y Y Medical Segmentation
56 refinedet_baseline Y Y Object Detection
57 resnet50_pt N/A Y Image Classification
58 squeezenet_pt N/A Y
59 inception_v3_pt N/A Y
  1. No1-No19 neural network models are trained based on the TensorFlow framework.
  2. No20-No56 neural network models are trained based on the Caffe framework.
  3. No57-No59 neural network models are trained based on the PyTorch framework.

Device Support

The following platforms and EVBs are supported by the Vitis™ AI Library 1.2.

Table 2. Edge Device Support
Platform EVB Version
Zynq UltraScale+ MPSoC ZU9EG Xilinx ZCU102 V1.1
Zynq® UltraScale+™ MPSoC ZU7EV Xilinx ZCU104 V1.0
Table 3. Cloud Device Support
Accelerator Cards
Xilinx Alveo U50
Xilinx Alveo U50lv
Xilinx Alveo U280

Limitations

  • Some neural networks with mobilenet as the backbone are not supported on U50, U50lv, and U280.
  • PyTorch models are not supported for edge devices.
  • Due to limitations of the Docker environment, Multi-Task demos cannot run in the DRM mode on Cloud devices.