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+# Benchmark
+
+We compare our results with some popular frameworks and official releases in terms of speed and accuracy.
+
+## Comparison Rules
+
+Here we compare our MMPose repo with other pose estimation toolboxes in the same data and model settings.
+
+To ensure the fairness of the comparison, the comparison experiments were conducted under the same hardware environment and using the same dataset.
+For each model setting, we kept the same data pre-processing methods to make sure the same feature input.
+In addition, we also used Memcached, a distributed memory-caching system, to load the data in all the compared toolboxes.
+This minimizes the IO time during benchmark.
+
+The time we measured is the average training time for an iteration, including data processing and model training.
+The training speed is measure with s/iter. The lower, the better.
+
+### Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset
+
+We demonstrate the superiority of our MMPose framework in terms of speed and accuracy on the standard COCO keypoint detection benchmark.
+The mAP (the mean average precision) is used as the evaluation metric.
+
+| Model | Input size| MMPose (s/iter) | HRNet (s/iter) | MMPose (mAP) | HRNet (mAP) |
+| :--- | :---------------: | :---------------: |:--------------------: | :----------------------------: | :-----------------: |
+| resnet_50  | 256x192  | **0.28** | 0.64 | **0.718** | 0.704 |
+| resnet_50  | 384x288  | **0.81** | 1.24 | **0.731** | 0.722 |
+| resnet_101 | 256x192  | **0.36** | 0.84 | **0.726** | 0.714 |
+| resnet_101 | 384x288  | **0.79** | 1.53 | **0.748** | 0.736 |
+| resnet_152 | 256x192  | **0.49** | 1.00 | **0.735** | 0.720 |
+| resnet_152 | 384x288  | **0.96** | 1.65 | **0.750** | 0.743 |
+| hrnet_w32  | 256x192  | **0.54** | 1.31 | **0.746** | 0.744 |
+| hrnet_w32  | 384x288  | **0.76** | 2.00 | **0.760** | 0.758 |
+| hrnet_w48  | 256x192  | **0.66** | 1.55 | **0.756** | 0.751 |
+| hrnet_w48  | 384x288  | **1.23** | 2.20 | **0.767** | 0.763 |
+
+## Hardware
+
+- 8 NVIDIA Tesla V100 (32G) GPUs
+- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+
+## Software Environment
+
+- Python 3.7
+- PyTorch 1.4
+- CUDA 10.1
+- CUDNN 7.6.03
+- NCCL 2.4.08