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+# Performance Profiling for TensorRT Library
+
+## Test Environment
+- CPU:Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU:Tesla P4
+- TensorRT4.0, CUDA8.0, CUDNNV7
+- Test model ResNet50, MobileNet, ResNet101, Inception V3.
+
+## Test Targets
+**PaddlePaddle, Pytorch, Tensorflow**   
+
+- In test, PaddlePaddle adopts subgraph optimization to integrate TensorRT [model](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models) .
+- Native implementation is used in Pytorch. Model [address 1](https://github.com/pytorch/vision/tree/master/torchvision/models) , [address 2](https://github.com/marvis/pytorch-mobilenet) .
+- Test for TensorFlow contains test for native TF and TF—TRT. **Test for TF—TRT hasn't reached expectation wihch will be complemented later**. Model [address](https://github.com/tensorflow/models) .
+
+
+### ResNet50 
+ 
+|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)|
+|---|---|---|---|
+|1|4.64117 |16.3|10.878|
+|5|6.90622| 22.9 |20.62|
+|10|7.9758 |40.6|34.36|
+
+### MobileNet
+|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)|
+|---|---|---|---|
+|1| 1.7541 | 7.8 |2.72|
+|5| 3.04666 | 7.8 |3.19|
+|10|4.19478 | 14.47 |4.25|
+
+### ResNet101
+|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)|
+|---|---|---|---|
+|1|8.95767| 22.48 |18.78|
+|5|12.9811 | 33.88 |34.84|
+|10|14.1463| 61.97 |57.94|
+
+
+### Inception v3
+|batch_size|PaddlePaddle(ms)|Pytorch(ms)|TensorFlow(ms)|
+|---|---|---|---|
+|1|15.1613 | 24.2 |19.1|
+|5|18.5373 | 34.8 |27.2|
+|10|19.2781| 54.8 |36.7|
+
+
+
+