# Benchmark and Model Zoo

## Environment

### Hardware

- 8 NVIDIA Tesla V100 GPUs
- Intel Xeon 4114 CPU @ 2.20GHz

### Software environment

- Python 3.6 / 3.7
- PyTorch 1.1
- CUDA 9.0.176
- CUDNN 7.0.4
- NCCL 2.1.15

## Mirror sites

We use AWS as the main site to host our model zoo, and maintain a mirror on aliyun.
You can replace `https://s3.ap-northeast-2.amazonaws.com/open-mmlab` with `https://open-mmlab.oss-cn-beijing.aliyuncs.com` in model urls.

## Common settings

- All FPN baselines and RPN-C4 baselines were trained using 8 GPU with a batch size of 16 (2 images per GPU). Other C4 baselines were trained using 8 GPU with a batch size of 8 (1 image per GPU).
- All models were trained on `coco_2017_train`, and tested on the `coco_2017_val`.
- We use distributed training and BN layer stats are fixed.
- We adopt the same training schedules as Detectron. 1x indicates 12 epochs and 2x indicates 24 epochs, which corresponds to slightly less iterations than Detectron and the difference can be ignored.
- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo.
- For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows.
- We report the inference time as the overall time including data loading, network forwarding and post processing.


## Baselines

More models with different backbones will be added to the model zoo.

### RPN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR1000 |                                                          Download                                                          |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------: |
|     R-50-C4     |  caffe  |   1x    |    -     |          -          |      20.5      |  51.1  |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_caffe_c4_1x-ea7d3428.pth)       |
|     R-50-C4     |  caffe  |   2x    |   2.2    |        0.17         |      20.3      |  52.2  |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_caffe_c4_2x-c6d5b958.pth)       |
|     R-50-C4     | pytorch |   1x    |    -     |          -          |      20.1      |  50.2  |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_c4_1x-eb38972b.pth)          |
|     R-50-C4     | pytorch |   2x    |    -     |          -          |      20.0      |  51.1  |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_c4_2x-3d4c1e14.pth)          |
|    R-50-FPN     |  caffe  |   1x    |   3.3    |        0.253        |      16.9      |  58.2  |                                                             -                                                              |
|    R-50-FPN     | pytorch |   1x    |   3.5    |        0.276        |      17.7      |  57.1  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010-4a9c0712.pth)     |
|    R-50-FPN     | pytorch |   2x    |    -     |          -          |       -        |  57.6  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010-88a4a471.pth)     |
|    R-101-FPN    |  caffe  |   1x    |   5.2    |        0.379        |      13.9      |  59.4  |                                                             -                                                              |
|    R-101-FPN    | pytorch |   1x    |   5.4    |        0.396        |      14.4      |  58.6  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_1x_20181129-f50da4bd.pth)    |
|    R-101-FPN    | pytorch |   2x    |    -     |          -          |       -        |  59.1  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_2x_20181129-e42c6c9a.pth)    |
| X-101-32x4d-FPN | pytorch |   1x    |   6.6    |        0.589        |      11.8      |  59.4  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_1x_20181218-7e379d26.pth) |
| X-101-32x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  59.9  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_2x_20181218-0510af40.pth) |
| X-101-64x4d-FPN | pytorch |   1x    |   9.5    |        0.955        |      8.3       |  59.8  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_1x_20181218-c1a24f1f.pth) |
| X-101-64x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  60.0  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_2x_20181218-c22bdd70.pth) |

### Faster R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                                                              Download                                                              |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------------------: |
|     R-50-C4     |  caffe  |   1x    |    -     |          -          |      9.5       |  34.9  |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_caffe_c4_1x-75ecfdfa.pth)       |
|     R-50-C4     |  caffe  |   2x    |   4.0    |        0.39         |      9.3       |  36.5  |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_caffe_c4_2x-71c67f27.pth)       |
|     R-50-C4     | pytorch |   1x    |    -     |          -          |      9.3       |  33.9  |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_c4_1x-642cf91f.pth)          |
|     R-50-C4     | pytorch |   2x    |    -     |          -          |      9.4       |  35.9  |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_c4_2x-6e4fdf4f.pth)          |
|    R-50-FPN     |  caffe  |   1x    |   3.6    |        0.333        |      13.5      |  36.6  |                                                                 -                                                                  |
|    R-50-FPN     | pytorch |   1x    |   3.8    |        0.353        |      13.6      |  36.4  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth)     |
|    R-50-FPN     | pytorch |   2x    |    -     |          -          |       -        |  37.7  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010-443129e1.pth)     |
|    R-101-FPN    |  caffe  |   1x    |   5.5    |        0.465        |      11.5      |  38.8  |                                                                 -                                                                  |
|    R-101-FPN    | pytorch |   1x    |   5.7    |        0.474        |      11.9      |  38.5  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_1x_20181129-d1468807.pth)    |
|    R-101-FPN    | pytorch |   2x    |    -     |          -          |       -        |  39.4  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_2x_20181129-73e7ade7.pth)    |
| X-101-32x4d-FPN | pytorch |   1x    |   6.9    |        0.672        |      10.3      |  40.1  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_1x_20181218-ad81c133.pth) |
| X-101-32x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  40.4  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_2x_20181218-0ed58946.pth) |
| X-101-64x4d-FPN | pytorch |   1x    |   9.8    |        1.040        |      7.3       |  41.3  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_1x_20181218-c9c69c8f.pth) |
| X-101-64x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  40.7  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_2x_20181218-fe94f9b8.pth) |
|   HRNetV2p-W18   | pytorch |   1x    |    -     |          -          |       -        |  36.1  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_1x_20190522-e368c387.pth)    |
|   HRNetV2p-W18   | pytorch |   2x    |    -     |          -          |       -        |  38.3  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_2x_20190810-9c8615d5.pth) |
|   HRNetV2p-W32   | pytorch |   1x    |    -     |          -          |       -        |  39.5  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_1x_20190522-d22f1fef.pth)    |
|   HRNetV2p-W32   | pytorch |   2x    |    -     |          -          |       -        |  40.6  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_2x_20190810-24e8912a.pth) |
|   HRNetV2p-W48   | pytorch |   1x    |    -     |          -          |       -        |  40.9  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_1x_20190820-5c6d0903.pth)    |
|   HRNetV2p-W48   | pytorch |   2x    |    -     |          -          |       -        |  41.5  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_2x_20190820-79fb8bfc.pth) |


### Mask R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP |                                                             Download                                                             |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------: |
|     R-50-C4     |  caffe  |   1x    |    -     |          -          |      8.1       |  35.9  |  31.5   |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_caffe_c4_1x-02a4ad3b.pth)       |
|     R-50-C4     |  caffe  |   2x    |   4.2    |        0.43         |      8.1       |  37.9  |  32.9   |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_caffe_c4_2x-d150973a.pth)       |
|     R-50-C4     | pytorch |   1x    |    -     |          -          |      7.9       |  35.1  |  31.2   |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_c4_1x-a83bdd40.pth)          |
|     R-50-C4     | pytorch |   2x    |    -     |          -          |      8.0       |  37.2  |  32.5   |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_c4_2x-3cf169a9.pth)          |
|    R-50-FPN     |  caffe  |   1x    |   3.8    |        0.430        |      10.2      |  37.4  |  34.3   |                                                                -                                                                 |
|    R-50-FPN     | pytorch |   1x    |   3.9    |        0.453        |      10.6      |  37.3  |  34.2   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth)     |
|    R-50-FPN     | pytorch |   2x    |    -     |          -          |       -        |  38.5  |  35.1   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010-41d35c05.pth)     |
|    R-101-FPN    |  caffe  |   1x    |   5.7    |        0.534        |      9.4       |  39.9  |  36.1   |                                                                -                                                                 |
|    R-101-FPN    | pytorch |   1x    |   5.8    |        0.571        |      9.5       |  39.4  |  35.9   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_1x_20181129-34ad1961.pth)    |
|    R-101-FPN    | pytorch |   2x    |    -     |          -          |       -        |  40.3  |  36.5   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_2x_20181129-a254bdfc.pth)    |
| X-101-32x4d-FPN | pytorch |   1x    |   7.1    |        0.759        |      8.3       |  41.1  |  37.1   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_1x_20181218-44e635cc.pth) |
| X-101-32x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  41.4  |  37.1   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_2x_20181218-f023dffa.pth) |
| X-101-64x4d-FPN | pytorch |   1x    |   10.0   |        1.102        |      6.5       |  42.1  |  38.0   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_1x_20181218-cb159987.pth) |
| X-101-64x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  42.0  |  37.7   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_2x_20181218-ea936e44.pth) |
|   HRNetV2p-W18   | pytorch |   1x    |    -     |          -          |       -        |  37.3  |  34.2   |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_1x_20190522-c8ad459f.pth)    |
|   HRNetV2p-W18   | pytorch |   2x    |    -     |          -          |       -        |  39.2  |  35.7   |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_2x_20190810-1e4747eb.pth)   |
|   HRNetV2p-W32   | pytorch |   1x    |    -     |          -          |       -        |  40.7  |  36.8   |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_1x_20190522-374aaa00.pth)    |
|   HRNetV2p-W32   | pytorch |   2x    |    -     |          -          |       -        |  41.7  |  37.5   |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_2x_20190810-773eca75.pth) |
|   HRNetV2p-W48   | pytorch |   1x    |    -     |          -          |       -        |  42.4  |  38.1   |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_1x_20190820-0923d1ad.pth) |
|   HRNetV2p-W48   | pytorch |   2x    |    -     |          -          |       -        |  42.9  |  38.3   |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_2x_20190820-70df51b2.pth) |

### Fast R-CNN (with pre-computed proposals)

| Backbone  |  Style  |  Type  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP |                                                            Download                                                             |
| :-------: | :-----: | :----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: |
|  R-50-C4  |  caffe  | Faster |   1x    |    -     |          -          |      6.7       |  35.0  |    -    |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_caffe_c4_1x-0ef9a60b.pth)      |
|  R-50-C4  |  caffe  | Faster |   2x    |   3.8    |        0.34         |      6.6       |  36.4  |    -    |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_c4_2x-657a9fc6.pth)         |
|  R-50-C4  | pytorch | Faster |   1x    |    -     |          -          |      6.3       |  34.2  |    -    |         [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_c4_1x-2bc00ca9.pth)         |
|  R-50-C4  | pytorch | Faster |   2x    |    -     |          -          |      6.1       |  35.8  |    -    |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_caffe_c4_2x-9171d0fc.pth)      |
| R-50-FPN  |  caffe  | Faster |   1x    |   3.3    |        0.242        |      18.4      |  36.6  |    -    |                                                                -                                                                |
| R-50-FPN  | pytorch | Faster |   1x    |   3.5    |        0.250        |      16.5      |  35.8  |    -    |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010-08160859.pth)    |
|  R-50-C4  |  caffe  |  Mask  |   1x    |    -     |          -          |      8.1       |  35.9  |  31.5   |   [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_caffe_c4_1x-b43f7f3c.pth)    |
|  R-50-C4  |  caffe  |  Mask  |   2x    |   4.2    |        0.43         |      8.1       |  37.9  |  32.9   |   [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_caffe_c4_2x-e3580184.pth)    |
|  R-50-C4  | pytorch |  Mask  |   1x    |    -     |          -          |      7.9       |  35.1  |  31.2   |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_c4_1x-bc7fa8c8.pth)       |
|  R-50-C4  | pytorch |  Mask  |   2x    |    -     |          -          |      8.0       |  37.2  |  32.5   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth)  |
| R-50-FPN  | pytorch | Faster |   2x    |    -     |          -          |       -        |  37.1  |    -    |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010-d263ada5.pth)    |
| R-101-FPN |  caffe  | Faster |   1x    |   5.2    |        0.355        |      14.4      |  38.6  |    -    |                                                                -                                                                |
| R-101-FPN | pytorch | Faster |   1x    |   5.4    |        0.388        |      13.2      |  38.1  |    -    |   [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_1x_20181129-ffaa2eb0.pth)    |
| R-101-FPN | pytorch | Faster |   2x    |    -     |          -          |       -        |  38.8  |    -    |   [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_2x_20181129-9dba92ce.pth)    |
| R-50-FPN  |  caffe  |  Mask  |   1x    |   3.4    |        0.328        |      12.8      |  37.3  |  34.5   |                                                                -                                                                |
| R-50-FPN  | pytorch |  Mask  |   1x    |   3.5    |        0.346        |      12.7      |  36.8  |  34.1   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010-e030a38f.pth)  |
| R-50-FPN  | pytorch |  Mask  |   2x    |    -     |          -          |       -        |  37.9  |  34.8   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth)  |
| R-101-FPN |  caffe  |  Mask  |   1x    |   5.2    |        0.429        |      11.2      |  39.4  |  36.1   |                                                                -                                                                |
| R-101-FPN | pytorch |  Mask  |   1x    |   5.4    |        0.462        |      10.9      |  38.9  |  35.8   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_1x_20181129-2273fa9b.pth) |
| R-101-FPN | pytorch |  Mask  |   2x    |    -     |          -          |       -        |  39.9  |  36.4   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_2x_20181129-bf63ec5e.pth) |

### RetinaNet

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                                                             Download                                                             |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------: |
|    R-50-FPN     |  caffe  |   1x    |   3.4    |        0.285        |      12.5      |  35.8  |                                                                -                                                                 |
|    R-50-FPN     | pytorch |   1x    |   3.6    |        0.308        |      12.1      |  35.6  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth)     |
|    R-50-FPN     | pytorch |   2x    |    -     |          -          |       -        |  36.4  |    [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/retinanet_r50_fpn_2x_20190616-75574209.pth)     |
|    R-101-FPN    |  caffe  |   1x    |   5.3    |        0.410        |      10.4      |  37.8  |                                                                -                                                                 |
|    R-101-FPN    | pytorch |   1x    |   5.5    |        0.429        |      10.9      |  37.7  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f016f384.pth)    |
|    R-101-FPN    | pytorch |   2x    |    -     |          -          |       -        |  38.1  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-72c14526.pth)    |
| X-101-32x4d-FPN | pytorch |   1x    |   6.7    |        0.632        |      9.3       |  39.0  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20190501-967812ba.pth) |
| X-101-32x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  39.3  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-8596452d.pth) |
| X-101-64x4d-FPN | pytorch |   1x    |   9.6    |        0.993        |      7.0       |  40.0  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-a0a22662.pth) |
| X-101-64x4d-FPN | pytorch |   2x    |    -     |          -          |       -        |  39.6  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-5e88d045.pth) |

### Cascade R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                                                              Download                                                               |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: |
|     R-50-C4     |  caffe  |   1x    |   8.7    |        0.92         |      5.0       |  38.7  |      [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_caffe_c4_1x-7c85c62b.pth)       |
|    R-50-FPN     |  caffe  |   1x    |   3.9    |        0.464        |      10.9      |  40.5  |                                                                  -                                                                  |
|    R-50-FPN     | pytorch |   1x    |   4.1    |        0.455        |      11.9      |  40.4  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_1x_20190501-3b6211ab.pth)     |
|    R-50-FPN     | pytorch |   20e   |    -     |          -          |       -        |  41.1  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth)    |
|    R-101-FPN    |  caffe  |   1x    |   5.8    |        0.569        |      9.6       |  42.4  |                                                                  -                                                                  |
|    R-101-FPN    | pytorch |   1x    |   6.0    |        0.584        |      10.3      |  42.0  |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_1x_20181129-d64ebac7.pth)    |
|    R-101-FPN    | pytorch |   20e   |    -     |          -          |       -        |  42.5  |   [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_20e_20181129-b46dcede.pth)    |
| X-101-32x4d-FPN | pytorch |   1x    |   7.2    |        0.770        |      8.9       |  43.6  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_1x_20190501-af628be5.pth) |
| X-101-32x4d-FPN | pytorch |   20e   |    -     |          -          |       -        |  44.0  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_2x_20181218-28f73c4c.pth) |
| X-101-64x4d-FPN | pytorch |   1x    |   10.0   |        1.133        |      6.7       |  44.5  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_1x_20181218-e2dc376a.pth) |
| X-101-64x4d-FPN | pytorch |   20e   |    -     |          -          |       -        |  44.7  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_2x_20181218-5add321e.pth) |
|   HRNetV2p-W18   | pytorch |   20e   |    -     |          -          |       -        |  41.2  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w18_20e_20190810-132012d0.pth) |
|   HRNetV2p-W32   | pytorch |   20e   |    -     |          -          |       -        |  43.7  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w32_20e_20190522-55bec4ee.pth)|
|   HRNetV2p-W48   | pytorch |   20e   |    -     |          -          |       -        |  44.6  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w48_20e_20190810-f40ed8e1.pth) |

### Cascade Mask R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP |                                                                 Download                                                                  |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------: |
|     R-50-C4     |  caffe  |   1x    |   9.1    |        0.99         |      4.5       |  39.3  |  32.8   |       [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_caffe_c4_1x-f72cc254.pth)       |
|    R-50-FPN     |  caffe  |   1x    |   5.1    |        0.692        |      7.6       |  40.9  |  35.5   |                                                                     -                                                                     |
|    R-50-FPN     | pytorch |   1x    |   5.3    |        0.683        |      7.4       |  41.2  |  35.7   |     [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth)     |
|    R-50-FPN     | pytorch |   20e   |    -     |          -          |       -        |  42.3  |  36.6   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_20e_20181123-6e0c9713.pth)     |
|    R-101-FPN    |  caffe  |   1x    |   7.0    |        0.803        |      7.2       |  43.1  |  37.2   |                                                                     -                                                                     |
|    R-101-FPN    | pytorch |   1x    |   7.2    |        0.807        |      6.8       |  42.6  |  37.0   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_1x_20181129-64f00602.pth)     |
|    R-101-FPN    | pytorch |   20e   |    -     |          -          |       -        |  43.3  |  37.6   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_20e_20181129-cb85151d.pth)    |
| X-101-32x4d-FPN | pytorch |   1x    |   8.4    |        0.976        |      6.6       |  44.4  |  38.2   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_1x_20181218-1d944c89.pth)  |
| X-101-32x4d-FPN | pytorch |   20e   |    -     |          -          |       -        |  44.7  |  38.6   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_20e_20181218-761a3473.pth) |
| X-101-64x4d-FPN | pytorch |   1x    |   11.4   |        1.33         |      5.3       |  45.4  |  39.1   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_1x_20190501-827e0a70.pth)  |
| X-101-64x4d-FPN | pytorch |   20e   |    -     |          -          |       -        |  45.7  |  39.4   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_20e_20181218-630773a7.pth) |
|   HRNetV2p-W18   | pytorch |   20e   |    -     |          -          |       -        |  41.9  |  36.4   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_20190810-054fb7bf.pth) |
|   HRNetV2p-W32   | pytorch |   20e   |    -     |          -          |       -        |  44.5  |  38.5   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_20190810-76f61cd0.pth) |
|   HRNetV2p-W48   | pytorch |   20e   |    -     |          -          |       -        |  46.0  |  39.5   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w48_20e_20190810-d04a1415.pth) |

**Notes:**

- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs.

### Hybrid Task Cascade (HTC)

|    Backbone     |  Style  | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP |                                                            Download                                                             |
| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: |
|    R-50-FPN     | pytorch |   1x    |   7.4    |        0.936        |      4.1       |  42.1  |  37.3   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_1x_20190408-878c1712.pth)     |
|    R-50-FPN     | pytorch |   20e   |    -     |          -          |       -        |  43.2  |  38.1   |    [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_20e_20190408-c03b7015.pth)     |
|    R-101-FPN    | pytorch |   20e   |   9.3    |        1.051        |      4.0       |  44.9  |  39.4   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r101_fpn_20e_20190408-a2e586db.pth)    |
| X-101-32x4d-FPN | pytorch |   20e   |   5.8    |        0.769        |      3.8       |  46.1  |  40.3   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_32x4d_fpn_20e_20190408-9eae4d0b.pth) |
| X-101-64x4d-FPN | pytorch |   20e   |   7.5    |        1.120        |      3.5       |  46.9  |  40.8   | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_64x4d_fpn_20e_20190408-497f2561.pth) |
|   HRNetV2p-W18   | pytorch |   20e   |    -     |          -          |       -        |  43.1  |  37.9   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w18_20e_20190810-d70072af.pth) |
|   HRNetV2p-W32   | pytorch |   20e   |    -     |          -          |       -        |  45.3  |  39.6   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w32_20e_20190810-82f9ef5a.pth) |
|   HRNetV2p-W48   | pytorch |   20e   |    -     |          -          |       -        |  46.8  | 40.7    | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_20e_20190810-f6d2c3fd.pth) |
|   HRNetV2p-W48   | pytorch |   28e   |    -     |          -          |       -        |  47.0  |  41.0   | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_28e_20190810-a4274b38.pth) |

**Notes:**

- Please refer to [Hybrid Task Cascade](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc) for details and more a powerful model (50.7/43.9).

### SSD

| Backbone | Size  | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                                                             Download                                                              |
| :------: | :---: | :---: | :-----: | :------: | :-----------------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: |
|  VGG16   |  300  | caffe |  120e   |   3.5    |        0.256        |  25.9 / 34.6   |  25.7  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) |
|  VGG16   |  512  | caffe |  120e   |   7.6    |        0.412        |  20.7 / 25.4   |  29.3  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd512_coco_vgg16_caffe_120e_20181221-d48b0be8.pth) |

**Notes:**

- `cudnn.benchmark` is set as `True` for SSD training and testing.
- Inference time is reported for batch size = 1 and batch size = 8.
- The speed on COCO and VOC are different due to model parameters and nms.

### Group Normalization (GN)

Please refer to [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn) for details.

### Weight Standardization

Please refer to [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws) for details.

### Deformable Convolution v2

Please refer to [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn) for details.

### Instaboost

Please refer to [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost) for details.

### Libra R-CNN

Please refer to [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn) for details.

### Guided Anchoring

Please refer to [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring) for details.

### FCOS

Please refer to [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos) for details.

### FoveaBox

Please refer to [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox) for details.

### RepPoints

Please refer to [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints) for details.

### FreeAnchor

Please refer to [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor) for details.

### Grid R-CNN (plus)

Please refer to [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn) for details.

### GHM

Please refer to [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm) for details.

### GCNet

Please refer to [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet) for details.

### HRNet
Please refer to [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet) for details.

### Mask Scoring R-CNN

Please refer to [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn) for details.

### Train from Scratch

Please refer to [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch) for details.

### NAS-FPN
Please refer to [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn) for details.

### ATSS
Please refer to [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss) for details.

### Other datasets

We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face).


## Comparison with Detectron and maskrcnn-benchmark

We compare mmdetection with [Detectron](https://github.com/facebookresearch/Detectron)
and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). The backbone used is R-50-FPN.

In general, mmdetection has 3 advantages over Detectron.

- **Higher performance** (especially in terms of mask AP)
- **Faster training speed**
- **Memory efficient**

### Performance

Detectron and maskrcnn-benchmark use caffe-style ResNet as the backbone.
We report results using both caffe-style (weights converted from
[here](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models))
and pytorch-style (weights from the official model zoo) ResNet backbone,
indicated as *pytorch-style results* / *caffe-style results*.

We find that pytorch-style ResNet usually converges slower than caffe-style ResNet,
thus leading to slightly lower results in 1x schedule, but the final results
of 2x schedule is higher.

<table>
  <tr>
    <th>Type</th>
    <th>Lr schd</th>
    <th>Detectron</th>
    <th>maskrcnn-benchmark</th>
    <th>mmdetection</th>
  </tr>
  <tr>
    <td rowspan="2">RPN</td>
    <td>1x</td>
    <td>57.2</td>
    <td>-</td>
    <td>57.1 / 58.2</td>
  </tr>
  <tr>
    <td>2x</td>
    <td>-</td>
    <td>-</td>
    <td>57.6 / -</td>
  </tr>
  <tr>
    <td rowspan="2">Faster R-CNN</td>
    <td>1x</td>
    <td>36.7</td>
    <td>36.8</td>
    <td>36.4 / 36.6</td>
  </tr>
  <tr>
    <td>2x</td>
    <td>37.9</td>
    <td>-</td>
    <td>37.7 / -</td>
  </tr>
  <tr>
    <td rowspan="2">Mask R-CNN</td>
    <td>1x</td>
    <td>37.7 &amp; 33.9</td>
    <td>37.8 &amp; 34.2</td>
    <td>37.3 &amp; 34.2 / 37.4 &amp; 34.3</td>
  </tr>
  <tr>
    <td>2x</td>
    <td>38.6 &amp; 34.5</td>
    <td>-</td>
    <td>38.5 &amp; 35.1 / -</td>
  </tr>
  <tr>
    <td rowspan="2">Fast R-CNN</td>
    <td>1x</td>
    <td>36.4</td>
    <td>-</td>
    <td>35.8 / 36.6</td>
  </tr>
  <tr>
    <td>2x</td>
    <td>36.8</td>
    <td>-</td>
    <td>37.1 / -</td>
  </tr>
  <tr>
    <td rowspan="2">Fast R-CNN (w/mask)</td>
    <td>1x</td>
    <td>37.3 &amp; 33.7</td>
    <td>-</td>
    <td>36.8 &amp; 34.1 / 37.3 &amp; 34.5</td>
  </tr>
  <tr>
    <td>2x</td>
    <td>37.7 &amp; 34.0</td>
    <td>-</td>
    <td>37.9 &amp; 34.8 / -</td>
  </tr>
</table>

### Training Speed

The training speed is measure with s/iter. The lower, the better.

<table>
  <tr>
    <th>Type</th>
    <th>Detectron (P100<sup>1</sup>)</th>
    <th>maskrcnn-benchmark (V100)</th>
    <th>mmdetection (V100<sup>2</sup>)</th>
  </tr>
  <tr>
    <td>RPN</td>
    <td>0.416</td>
    <td>-</td>
    <td>0.253</td>
  </tr>
  <tr>
    <td>Faster R-CNN</td>
    <td>0.544</td>
    <td>0.353</td>
    <td>0.333</td>
  </tr>
  <tr>
    <td>Mask R-CNN</td>
    <td>0.889</td>
    <td>0.454</td>
    <td>0.430</td>
  </tr>
  <tr>
    <td>Fast R-CNN</td>
    <td>0.285</td>
    <td>-</td>
    <td>0.242</td>
  </tr>
  <tr>
    <td>Fast R-CNN (w/mask)</td>
    <td>0.377</td>
    <td>-</td>
    <td>0.328</td>
  </tr>
</table>

\*1. Facebook's Big Basin servers (P100/V100) is slightly faster than the servers we use. mmdetection can also run slightly faster on FB's servers.

\*2. For fair comparison, we list the caffe-style results here.


### Inference Speed

The inference speed is measured with fps (img/s) on a single GPU. The higher, the better.

<table>
  <tr>
    <th>Type</th>
    <th>Detectron (P100)</th>
    <th>maskrcnn-benchmark (V100)</th>
    <th>mmdetection (V100)</th>
  </tr>
  <tr>
    <td>RPN</td>
    <td>12.5</td>
    <td>-</td>
    <td>16.9</td>
  </tr>
  <tr>
    <td>Faster R-CNN</td>
    <td>10.3</td>
    <td>7.9</td>
    <td>13.5</td>
  </tr>
  <tr>
    <td>Mask R-CNN</td>
    <td>8.5</td>
    <td>7.7</td>
    <td>10.2</td>
  </tr>
  <tr>
    <td>Fast R-CNN</td>
    <td>12.5</td>
    <td>-</td>
    <td>18.4</td>
  </tr>
  <tr>
    <td>Fast R-CNN (w/mask)</td>
    <td>9.9</td>
    <td>-</td>
    <td>12.8</td>
  </tr>
</table>

### Training memory

<table>
  <tr>
    <th>Type</th>
    <th>Detectron</th>
    <th>maskrcnn-benchmark</th>
    <th>mmdetection</th>
  </tr>
  <tr>
    <td>RPN</td>
    <td>6.4</td>
    <td>-</td>
    <td>3.3</td>
  </tr>
  <tr>
    <td>Faster R-CNN</td>
    <td>7.2</td>
    <td>4.4</td>
    <td>3.6</td>
  </tr>
  <tr>
    <td>Mask R-CNN</td>
    <td>8.6</td>
    <td>5.2</td>
    <td>3.8</td>
  </tr>
  <tr>
    <td>Fast R-CNN</td>
    <td>6.0</td>
    <td>-</td>
    <td>3.3</td>
  </tr>
  <tr>
    <td>Fast R-CNN (w/mask)</td>
    <td>7.9</td>
    <td>-</td>
    <td>3.4</td>
  </tr>
</table>

There is no doubt that maskrcnn-benchmark and mmdetection is more memory efficient than Detectron,
and the main advantage is PyTorch itself. We also perform some memory optimizations to push it forward.

Note that Caffe2 and PyTorch have different apis to obtain memory usage with different implementations.
For all codebases, `nvidia-smi` shows a larger memory usage than the reported number in the above table.
