Classification - 3.5 English

Vitis AI Library User Guide (UG1354)

Document ID
UG1354
Release Date
2023-06-29
Version
3.5 English

The Classification library is used to classify images. Such neural networks are trained on the 1000 class ILSVRC subset of the ImageNet dataset and can predict a per-class probability that the object in the image is a member of each class. The Vitis AI Library supports classification networks including, but not limited to, ResNet18, ResNet50, Inception_v1, Inception_v2, Inception_v3, Inception_v4, VGG, mobilenet_v1, mobilenet_v2, and Squeezenet. 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 mobilenet_v3_small_1_0_tf2
30 efficientnet_lite_tf2
31 resnet50 Caffe
32 resnet18
33 inception_v1
34 inception_v2
35 inception_v3
36 inception_v4
37 mobilenet_v2
38 squeezenet
39 resnet50_pt PyTorch
40 squeezenet_pt
41 inception_v3_pt
42 ofa_resnet50_0_9B_pt
43 person-orientation_pruned_558m_pt
44 ofa_depthwise_res50_pt
45 chen_color_resnet18_pt