Classification - 1.4.1 English

Vitis AI Library User Guide (UG1354)

Document ID
UG1354
Release Date
2021-12-11
Version
1.4.1 English

The Classification library is used to classify images. Such neural networks are trained on ImageNet for ILSVRC and they can identify the objects from its 1000 classification. The Vitis AI Library integrates networks including, but not limited to, ResNet18, ResNet50, Inception_v1, Inception_v2, Inception_v3, Inception_v4, Vgg, mobilenet_v1, mobilenet_v2, and Squeezenet into Xilinx libraries. The input is a picture with an object and the output is the top-K most probable category.

Figure 1. Classification Example

The following table lists the classification models supported by the Vitis AI library.

Table 1. Classification Models
No Model Name Framework
1 inception_resnet_v2_tf TensorFlow
2 inception_v1_tf
3 inception_v3_tf
4 inception_v4_2016_09_09_tf
5 mobilenet_v1_0_25_128_tf
6 mobilenet_v1_0_5_160_tf
7 mobilenet_v1_1_0_224_tf
8 mobilenet_v2_1_0_224_tf
9 mobilenet_v2_1_4_224_tf
10 resnet_v1_101_tf
11 resnet_v1_152_tf
12 resnet_v1_50_tf
13 vgg_16_tf
14 vgg_19_tf
15 mobilenet_edge_1_0_tf
16 mobilenet_edge_0_75_tf
17 inception_v2_tf
18 MLPerf_resnet50_v1.5_tf
19 resnet50_tf2
20 mobilenet_1_0_224_tf2
21 inception_v3_tf2
22 resnet_v2_50_tf
23 resnet_v2_101_tf
24 resnet_v2_152_tf
25 efficientnet-b0_tf2
26 efficientNet-edgetpu-S_tf
27 efficientNet-edgetpu-M_tf
28 efficientNet-edgetpu-L_tf
29 resnet50 Caffe
30 resnet18
31 inception_v1
32 inception_v2
33 inception_v3
34 inception_v4
35 mobilenet_v2
36 squeezenet
37 resnet50_pt PyTorch
38 squeezenet_pt
39 inception_v3_pt