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.
| 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 |