+ TFlite版本：来源于github的master branch中commits ID：65c05bc2ac19f51f7027e66350bc71652662125c（msg:Removed unneeded file copy that was causing failure in Pi builds)版本
> CPU: `12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz`
> GPU: `Tesla P4`
> cuDNN: `v7`
## Counterpart of anakin :
The counterpart of **`Anakin`** is the acknowledged high performance inference engine **`NVIDIA TensorRT 3`** , The models which TensorRT 3 doesn't support we use the custom plugins to support.
## Benchmark Model
The following convolutional neural networks are tested with both `Anakin` and `TenorRT3`.
You can use pretrained caffe model or the model trained by youself.
> Please note that you should transform caffe model or others into anakin model with the help of [`external converter ->`](../docs/Manual/Converter_en.md)
-[Vgg16](#1)*caffe model can be found [here->](https://gist.github.com/jimmie33/27c1c0a7736ba66c2395)*
-[Yolo](#2)*caffe model can be found [here->](https://github.com/hojel/caffe-yolo-model)*
-[Resnet50](#3)*caffe model can be found [here->](https://github.com/KaimingHe/deep-residual-networks#models)*
-[Resnet101](#4)*caffe model can be found [here->](https://github.com/KaimingHe/deep-residual-networks#models)*
-[Mobilenet v1](#5)*caffe model can be found [here->](https://github.com/shicai/MobileNet-Caffe)*
-[Mobilenet v2](#6)*caffe model can be found [here->](https://github.com/shicai/MobileNet-Caffe)*
-[RNN](#7)*not support yet*
We tested them on single-GPU with single-thread.
### <span id = '1'>VGG16 </span>
- Latency (`ms`) of different batch
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 8.8690 | 8.2815
2 | 15.5344 | 13.9116
4 | 26.6000 | 21.8747
8 | 49.8279 | 40.4076
32 | 188.6270 | 163.7660
- GPU Memory Used (`MB`)
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 963 | 997
2 | 965 | 1039
4 | 991 | 1115
8 | 1067 | 1269
32 | 1715 | 2193
### <span id = '2'>Yolo </span>
- Latency (`ms`) of different batch
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 16.4596| 15.2124
2 | 26.6347| 25.0442
4 | 43.3695| 43.5017
8 | 80.9139 | 80.9880
32 | 293.8080| 310.8810
- GPU Memory Used (`MB`)
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 1569 | 1775
2 | 1649 | 1815
4 | 1709 | 1887
8 | 1731 | 2031
32 | 2253 | 2907
### <span id = '3'> Resnet50 </span>
- Latency (`ms`) of different batch
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 4.2459 | 4.1061
2 | 6.2627 | 6.5159
4 | 10.1277 | 11.3327
8 | 17.8209 | 20.6680
32 | 65.8582 | 77.8858
- GPU Memory Used (`MB`)
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 531 | 503
2 | 543 | 517
4 | 583 | 541
8 | 611 | 589
32 | 809 | 879
### <span id = '4'> Resnet101 </span>
- Latency (`ms`) of different batch
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 7.5562 | 7.0837
2 | 11.6023 | 11.4079
4 | 18.3650 | 20.0493
8 | 32.7632 | 36.0648
32 | 123.2550 | 135.4880
- GPU Memory Used (`MB)`
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 701 | 683
2 | 713 | 697
4 | 793 | 721
8 | 819 | 769
32 | 1043 | 1059
### <span id = '5'> MobileNet V1 </span>
- Latency (`ms`) of different batch
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 45.5156 | 1.3947
2 | 46.5585 | 2.5483
4 | 48.4242 | 4.3404
8 | 52.7957 | 8.1513
32 | 83.2519 | 31.3178
- GPU Memory Used (`MB`)
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 329 | 283
2 | 345 | 289
4 | 371 | 299
8 | 393 | 319
32 | 531 | 433
### <span id = '6'> MobileNet V2</span>
- Latency (`ms`) of different batch
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 65.6861 | 2.9842
2 | 66.6814 | 4.7472
4 | 69.7114 | 7.4163
8 | 76.1092 | 12.8779
32 | 124.9810 | 47.2142
- GPU Memory Used (`MB`)
BatchSize | TensorRT | Anakin
:---: | :---: | :---: |
1 | 341 | 293
2 | 353 | 301
4 | 385 | 319
8 | 421 | 351
32 | 637 | 551
## How to run those Benchmark models?
> 1. At first, you should parse the caffe model with [`external converter ->`](../docs/Manual/Converter_en.md).
> 2. Switch to *source_root/benchmark/CNN* directory. Use 'mkdir ./models' to create ./models and put anakin models into this file.
> 3. Use command 'sh run.sh', we will create files in logs to save model log with different batch size. Finally, model latency summary will be displayed on the screen.
> 4. If you want to get more detailed information with op time, you can modify CMakeLists.txt with setting `ENABLE_OP_TIMER` to `YES`, then recompile and run. You will find detailed information in model log file.
