Add guidance for speed benchmark (#4537)

* add a guidance for benchmark speed

* add training time guidance

* fix typos

* fix url

* fix type

docs/model_zoo.md

@@ -193,6 +193,10 @@ We also train [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/mast
 
 ## Speed benchmark
 
+### Training Speed benchmark
+
+We provide [analyze_logs.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py) to get average time of iteration in training. You can find examples in [Log Analysis](https://mmdetection.readthedocs.io/en/latest/useful_tools.html#log-analysis).
+
 We compare the training speed of Mask R-CNN with some other popular frameworks (The data is copied from [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md)).
 For mmdetection, we benchmark with [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py), which should have the same setting with [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) of detectron2.
 We also provide the [checkpoint](http://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth) and [training log](http://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) for reference. The throughput is computed as the average throughput in iterations 100-500 to skip GPU warmup time.
@@ -207,6 +211,17 @@ We also provide the [checkpoint](http://download.openmmlab.com/mmdetection/v2.0/
 | [Detectron](https://github.com/facebookresearch/Detectron) | 19 |
 | [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/) | 14 |
 
+### Inference Speed Benchmark
+
+We provide [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) to benchmark the inference latency.
+The script benchmarkes the model with 2000 images and calculates the average time ignoring first 5 times. You can change the output log interval (defaults: 50) by setting `LOG-INTERVAL`.
+
+```shell
+python toools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL]] [--fuse-conv-bn]
+```
+
+The latency of all models in our model zoo is benchmarked without setting `fuse-conv-bn`, you can get a lower latency by setting it.
+
 ## Comparison with Detectron2
 
 We compare mmdetection with [Detectron2](https://github.com/facebookresearch/detectron2.git) in terms of speed and performance.