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.
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 |